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1,855 results for “framework”
Supplementary files for "Pressure-enhanced ferroelectric polarisation in polar perovskite-like [C2H5NH3]Na0.5Cr0.5(HCOO)3 metal-organic framework"
<p>DFT optimised structures for the paper: Pressure-enhanced ferroelectric polarisation in polar perovskite-like [C2H5NH3]Na0.5Cr0.5(HCOO)3 metal-organic framework. See the paper for additional information on the computational setup.</p> <p>Files are named HP or LP for high-pressure and low-pressure phases, followed by the pressure as calculated by DFT. The HP structure was optimized in two different space groups and are labelled accordingly. The DFT optimized structure without volume restrictions is found in opt-vol.POSCAR</p> <p> </p>
EC 5th Framework ENPOWER austenitic edge welded beam contour cut metrology for assessing residual stress
<p>Data from contour method cut surfaces collected from an autogenously edge-welded AISI 316H stainless steel beam produced as part of ENPOWER. Surfaces were generated as part of a slitting experiment and then subsequently measured with a coordinate measurement machine. This dataset forms the basis for <a href="https://doi.org/10.1115/1.4004626">"<em>Slitting and Contour Method Residual Stress Measurements in an Edge Welded Beam</em>" Hosseinzadeh et al. (2012)</a>, and further information on the specimen background and diffraction based results can be found in <a href="https://doi.org/10.1115/PVP2008-61339">"<em>A statistical framework for analysing weld residual stresses for structural integrity assessment</em>" Nadri et al. (2008)</a>.</p> <p>Datasets are in the form of lists of x,y.z coordinates, with one point per line, whitespace delimited in millimeters. The *Perimeter1.txt file coincides with *Surface1.txt, with the former an outline identifying the cut surface periphery, and the latter points lying on the surface. The same format is employed for the other side of the cut.</p>
Official GO FAIR Foundation icons for the Three-Point FAIRification Framework
<p>The official icons for the Three-Point FAIRification Framework (3PFF): Metadata for Machines Workshops, FAIR Implementation Profiles and FAIR Orchestration, created by the GO FAIR Foundation.</p>
Stratigraphic Framework of the US Eastern North American Margin
<p>The current dataset consists of seismic picks, and structural and isopach maps that depict the stratigraphic division of the sedimentary cover of the US part of the Eastern North American Margin. The stratigraphic analysis is based on tied seismic and well data spanning the continental shelf, slope and rise from Cape Hatteras to the US-Canada maritime border. In addition, it incorporates published hydrostratigraphic data that divide the coastal plain sedimentary cover. File formats are GeoTiff and Zmap+ (grids) and .CSV and .shp (points). Seismic interpretation, well analysis, stratigraphic correlation and data curation were done as part of a PhD study (Lang, 2023). </p>
Replication Package for the paper "Conversing with business process-aware Large Language Models: the BPLLM framework"
<p>Replication Package for the research paper "<em>Conversing with business process-aware Large Language Models: the BPLLM framework</em>".</p> <p>The package includes the process models, the questions (and expected answers), the results of the qualitative evaluation, and the Hugging Face links to the fine-tuned versions of Llama 3.1 8B employed in the quantitative evaluation of the framework.</p> <p>In particular, the process models are:</p> <ul> <li>The natural language Directly-follows graph (DFG) of the Food Delivery process: <em>food_delivery_activities.txt</em> for the definition of the activities and <em>food_delivery_flow.txt</em> for the sequence flow.</li> <li>The BPMN model of the Food Delivery, E-commerce, and Reimbursement processes: <em>ecommerce.bpmn</em>, <em>food_delivery.bpmn</em>, and <em>reimbursement.bpmn</em>.</li> </ul> <p>The datasets with the questions and the expected answers are:</p> <ul> <li><em>1_questions_answers_not_refined_for_DFG.csv</em> ;</li> <li><em>1.1_questions_answers_refined_for_DFG.csv</em> ;</li> <li><em>2_questions_answers_not_refined.csv</em> ;</li> <li><em>3_questions_answers_refined.csv</em> ;</li> <li><em>4_questions_answers_different_processes.csv</em> ;</li> <li><em>5_questions_answers_similar_processes.csv</em> ;</li> <li><em>6_questions_answers_refined_ft.csv</em> .</li> </ul> <p>The complete results of the qualitative evaluation are contained in the file <em>qualitative_experiments_results.pdf</em>.</p> <p>The Hugging Face links to the fine-tuned versions of Llama 3.1 8B are reported in <em>hf_links_finetuned_models.pdf</em>.</p>
ODRE Framework experiments
<p>This repository contains a dataset with ODRL policies and to test the ODRE implementations pyodre(https://github.com/ODRE-Framework/odre-python/tree/main) 1.0.3 and java-odre(https://github.com/ODRE-Framework/odre-java/tree/main) 0.1.1 along with the necessary instructions or code to test them.</p>
Advancing Vanadium Redox Flow Battery Analysis: A Deep Learning Framework for High-Throughput 3D Visualization and Bubble Quantification via Synchrotron X-ray Tomography
<p>Dataset and model of UTILE-Redox - Deep Learning based Tool for Autonomous 3D Bubble Analysis of Vanadium Flow Batteries from Synchrotron X-ray Imaging. This project focuses on the deep learning-based automatic analysis of Vanadium Redox Flow Batteries (VRFB) Synchrotron X-ray tomographies. This repository contains the Python implementation of the UTILE-Redox software for automatic volume analysis, feature extraction, and visualization of the results.</p>
Data from: Habitat suitability models reveal extensive distribution of deep warm water coral frameworks in the Red Sea
<p>Deep-sea coral frameworks are understudied in the Red Sea, where conditions in the deep are conspicuously warm and saline compared to other basins. Habitat suitability models can be used to predict the distribution pattern of species or assemblages where direct observation is difficult. Here we show how coral frameworks, built by species within the families Caryophylliidae and Dendrophylliidae, are distributed between water depths of 150 m and 700 m in the northern Red Sea and Gulf of Aqaba. To extrapolate the known (ground-truthed) positions of these deep frameworks, we use environmental and geomorphometric variables to inform well-performing maximum entropy models. Over 250 km2 of seafloor in our study area are identified as suitable for such frameworks, equivalent to at least 35% of the area of photic-zone coral reefs in the same region. We hence contend that deep-water coral frameworks are an important and underappreciated repository of Red Sea biodiversity.</p>
MEFI Framework for the Big Bang as a Supermassive Collapse
<p>This paper presents a fully balanced mathematical formulation of the Modified Einstein Field Interaction (MEFI) framework, which models the Big Bang as a supermassive collapse rather than a singular explosive event. The MEFI framework introduces four primary forces—repulsive forces, compressive dark fields, magneto electric fields, and quantum fluctuations (Delta Q)—to explain cosmic evolution. By balancing these forces, we derive a stable equation that governs both the collapse and expansion of the universe. The final force equation incorporates frequency resonance to regulate fluctuations, ensuring a harmonious interplay between these forces throughout cosmic evolution.</p>
Category Theory Framework for Variability Models with Non-functional Requirements @ CAiSE 21
<p><strong>Your can watch this video in my Youtube channel:</strong></p> <p><strong><a href="https://youtu.be/rX50Q3fpMZE">https://youtu.be/rX50Q3fpMZE</a></strong></p> <p><strong>This is a Live Conference Presentation, please access and cite the published version of the respective publication:</strong></p> <p><strong><a href="https://doi.org/10.1007/978-3-030-79382-1_24">https://doi.org/10.1007/978-3-030-79382-1_24</a></strong></p> <p>In Software Product Line (SPL) engineering one uses Variability Models (VMs) as input to automated reasoners to generate optimal products according to certain Quality Attributes (QAs). Variability models, however, and more specifically those including numerical features (i.e., NVMs), do not natively support QAs, and consequently, neither do automated reasoners commonly used for variability resolution. However, those satisfiability and optimisation problems have been covered and refined in other relational models such as databases. Category Theory (CT) is an abstract mathematical theory typically used to capture the common aspects of seemingly dissimilar algebraic structures. We propose a unified relational modelling framework subsuming the structured objects of VMs and QAs and their relationships into algebraic categories. This abstraction allows a combination of automated reasoners over different domains to analyse SPLs. The solutions’ optimisation can now be natively performed by a combination of automated theorem proving, hashing, balanced-trees and chasing algorithms. We validate this approach by means of the edge computing SPL tool HADAS.</p>
IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 2nd Web-Delphi process to HTA stakeholders, organized in a single panel
<p>IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 2<sup>nd</sup> Web-Delphi process to HTA stakeholders, organized in a single panel (all stakeholder groups in a single panel, 2 rounds), about the views of stakeholders regarding “This aspect should be considered in the evaluation of new medicines on a common basis” (2019)</p> <p>For details on the Web-Delphi process, see: IMPACT HTA, Work Package 7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2, Deliverable 7.2 (Multi-criteria evaluation framework), Advancing knowledge and MCDA tools to assist HTA agencies in evaluating medicines on a common basis (2021) Oliveira, M.D. (IST), Panos Kanavos (LSE), Bana e Costa, C. (IST)</p>
IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 1st Web-Delphi process to HTA stakeholders, organized into 6 separate parallel panels
<p>IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 1<sup>st</sup> Web-Delphi process to HTA stakeholders, organized into 6 separate parallel panels (one panel per stakeholder group, 2 rounds), about the views of stakeholders regarding “This aspect should be considered in the evaluation of new medicines on a common basis” (2019)</p> <p>For details on the Web-Delphi process, see: IMPACT HTA, Work Package 7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2, Deliverable 7.2 (Multi-criteria evaluation framework), Advancing knowledge and MCDA tools to assist HTA agencies in evaluating medicines on a common basis (2021) Oliveira, M.D. (IST), Panos Kanavos (LSE), Bana e Costa, C. (IST)</p>
Code and data for "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: A Case Study of Estimating N2O Emission using Data from Mesocosm Experiments "
<p>This is code and data for manuscript: <br> "KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: <br> A Case Study of Estimating N<sub>2</sub>O Emission using Data from Mesocosm Experiments"<br> Licheng Liu, Shaoming Xu, Zhenong Jin*, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, <br> Matt D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar</p> <p>All the files belong to Prof. Zhenong Jin, University of Minnesota, UA. jinzn@umn.edu<br> "code" foler includes code for data processing, model training, and results plotting.<br> "trained_model_saved" includes all trained model so you can use to reproduce the results showed in the study;<br> "data" includes all data presented in the study. Finetuning data is refering to Miller, L.T. , Griffis, T. J., Erickson, M. D., Turner, P. A., Deventer, M. J., Chen, Z., Yu, Z., Venterea, R.T., Baker, J. M., and Frie, A. L. (2021). Response of nitrous oxide emissions to future changes in precipitation and individual rain events. Journal of Environmental Quality, In review</p>
Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework"
<p><em>Amonthly_files.tar.gz</em> contains the gridded monthly averaged quantities used in the manuscript Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" for each year between 2000 and 2018.</p> <p>Files containing "simba" in their name contain quantities related to the sea ice mass balance (volume of melt/growth...)</p> <p>Files containing "icemod" in their name contain other quantities related to sea ice properties (thickness, concentration...)</p> <p>In case information is missing, do not hesitate to contact guillaume.boutin@nersc.no , heather.regan@nersc.no or einar.olason@nersc.no</p> <p>This research has been funded by the Norwegian Research Council (Nansen Legacy: grant no. 27673, FRASIL: grant no. 263044, and ARIA: grant no. 302934), JPI Climate and JPI Oceans (MEDLEY project, under agreement with the Norwegian Research Council, grant no 316730), and by Copernicus Marine Environment Monitoring Service (CMEMS) WIzARd project. CMEMS is implemented by Mercator Ocean in the framework of a delegation agreement with the European Union<br> Copernicus Marine Environment Monitoring Services (contract no.<br> 69), and the European Space Agency through the Cryosphere Virtual Laboratory (CVL, grant no. 4000128808/19/I-NS).</p>
NextFood Framework Metadata
<p>This dataset contains underlying metadata for the NextFood Sustainability Impact Assessment Framework, intended for the monitoring, reporting and evaluation of quality assured knowledge transfer through practice-oriented research and education. The approach is intended to support the planning for a desired impact, the inclusion of relevant actors, and the fostering of interaction and mutual learning between actors.</p>
A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission example data
<p>These files contain the Confluence pipeline outputs, prior information (SOS) and Simulated SWOT shape files from the example in the "A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission" manuscript. </p>
Framework for Assessing Changes To Sea-level (FACTS) Module Data - Part 2
<p>Additional input module data sets from the Framework for Assessing Changes To Sea-level. These files should be installed in the modules-data/ directory. See https://github.com/radical-collaboration/facts for more information.</p>
Data associated with "Developing a standardized but extendable framework to increase the findability of infectious disease datasets"
<p><strong>Data associated with "Developing a standardized but extendable framework to increase the findability of infectious disease datasets"</strong></p> <p> </p> <p>Includes:</p> <ul> <li>NIAID Dataset schema</li> <li>NIAID ComputationalTool schema</li> <li>Crosswalk between NIAID schemas and common schemas</li> <li>Survey of Schema.org-compliant repositories</li> </ul> <p><br> The open access movement and scientific reproducibility concerns have led the biomedical research community to embrace efforts to make scientific datasets openly accessible. While many datasets are now available, there are still challenges in ensuring that they are Findable, Accessible, Interoperable, and Reusable (FAIR). To improve the FAIRness of datasets, we evaluated dataset repositories for compliance with Schema.org standards – a collection of standards developed to increase metadata searchability across the internet. Adoption of the Schema.org Dataset standard was highly variable in biomedical research datasets, and the standard omitted many desirable metadata fields. We customized the Schema.org Dataset standard to catalog datasets collected across a Systems Biology research consortium consisting of 15 Centers. We developed a reusable process for creating a schema which is interoperable with other standards, but still extendable and customizable to a particular context. Here, we describe our process along with the associated gains in FAIRness, and discuss ongoing challenges with dataset discoverability – the first step to ensure that the vast amount of open data published by the research community is reused to its maximum value.</p>
Metal-organic frameworks as regeneration optimized sorbents for atmospheric water harvesting
<p>Dataset for 'Metal-organic frameworks as regeneration optimized sorbents for atmospheric water harvesting' article published at <em>Cell Reports Physical Science, </em><a href="https://doi.org/10.1016/j.xcrp.2023.101252">https://doi.org/10.1016/j.xcrp.2023.101252</a>.</p> <p>Code used for data analysis, visualization and kinetics modelling can be found at <a href="https://github.com/AndreyBezrukov/Water_Sorption_Kinetics">AndreyBezrukov/Water_Sorption_Kinetics (github.com)</a></p>
Datasets supplementing journal article "Probing dynamic covalent chemistry in a 2D boroxine framework by in-situ near-ambient pressure X-ray photoelectron spectroscopy" in Nanoscale 2022
<p>Datasets supporting the Nanoscale journal article "Probing dynamic covalent chemistry in a 2D boroxine framework by in-situ near-ambient pressure X-ray photoelectron spectroscopy".</p> <p>NAP-XPS.zip: Near-ambient pressure X-ray photoelectron spectroscopy, Figures 2, 3. (NEP 101007417)</p> <p>STM.zip: Scanning tunneling microscopy, Figure 5a, inset. (NEP 101007417)</p> <p>TPD.zip: Temperature programmed desorption, Figure 1a.</p> <p>UHV-XPS.zip: X-ray photoelectron spectroscopy, Figure 1b,c.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101007417, having benefited from the access provided by by ALBA in Barcelona (Spain) and CNR-IOM in Trieste (Italy) within the framework of the NFFA-Europe Pilot Transnational Access Activity, proposal ID075.</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.