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870 results for “Model development”
Deposition of data for developing deep learning models to assess crack width and self-healing progress in concrete (krkCMd)
<p>This is a deposition of data for developing deep learning models to assess crack width and self-healing progress in concrete [1]. It relates to an experimental study on the autogenous self-healing of high-strength concrete [2]. Concrete specimens were prepared, matured, cracked, and exposed to self-healing. High-resolution scanning of the specimen surface and scale-invariant image processing were performed, multiple grid lines crossing cracks were established, and brightness degree profiles were extracted. Then, manual measurements of the crack widths were obtained by an operator.</p> <p>The dataset comprises 19,098 records of brightness profiles, reference crack width measurements, and benchmark measurements by deep learning and analytic models. The source images, which were stacked and marked with grid lines, are provided. The considerable number of brightness profiles coupled with manual reference measurements make the dataset well suited for developing an image-based deep learning models or analytic algorithms for assessing crack widths in concrete.</p> <p>The deposited data includes:</p> <ul> <li>krkCMd_table.csv: delimited, comma-separated text file containing a dataset of 19,098 crack brightness degree profiles, reference crack width measurements by operator, and benchmark measurements by a deep CNN metasensor and by an analytic edge detector.</li> <li>krkCMd_images.zip: archive containing source image files in folders by test series: <br>- stacked images of cracks in subsequent stages of self-healing (.tif files),<br>- zip archives assigned to image stacks and containing sets of ImageJ data files .roi,<br>- ImageJ .roi files specifying the locations of grid lines in the images.</li> <li>krkCMd_scripts.zip: archive containing custom scripts supporting image preprocessing and computing benchmark variables.</li> </ul> <p><span>For details please see the <a href="https://doi.org/10.1038/s41597-025-04485-z">data descriptor [1]</a>. When referring to the data in publications please cite [1].</span></p> <p>[1] Jakubowski, J., Tomczak, K. Dataset for developing deep learning models to assess crack width and self-healing progress in concrete. <em>Sci Data</em> <strong>12</strong>, 165 (2025). https://doi.org/10.1038/s41597-025-04485-z</p> <p>[2] Jakubowski, J. & Tomczak, K. Deep learning metasensor for crack-width assessment and self-healing evaluation in concrete. <em>Constr. Build. Mater.</em> <strong>422</strong>, 135768 (2024). https://doi.org/10.1016/j.conbuildmat.2024.135768</p>
Data for: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling
<h2>Description</h2> <p>DATA REPOSITORY FOR</p> <p>Title: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and <br> multi-scale material modeling<br>By: Eva Jägle, Jithender J. Timothy, Daniel Jansen, Alisa Machner<br>Accepted by: Cement and Concrete Research</p> <p>This dataset presents the data of the paper 'Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling' submitted to and accepted by Cement and Concrete Research. The dataset follows the structure of the paper such that the calculations described therein can be reproduced.</p> <p>Data is available on three types of cement: Two ordinary Portland cements of different grinding fineness (CEM I 42.5 R und CEM I 52.5 R) and one limestone-containing blended cement (CEM II/A-LL 42.5 R). The data refer to the first 24 hours of hydration and temperature conditions of 20°C (for CEM I 42.5 R, CEM I 52.5 R, CEM II/A-LL 42.5 R) and 35°C (for CEM I 52.5 R). All data were retrieved for cement pastes with a water-to-cement ratio of 0.45.</p> <p>The dataset contains raw and processed data from quantitative X-ray diffraction, 5PL cement dissolution fitting, thermodynamic simulation with GEMS, multi-scale material modeling, ultrasonic testing and Vicat penetration tests. The data is mainly available in .xlsx files together with short descriptions in ReadMe.txt files.</p>
Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models
<p>This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 2. Developing prediction models" to be published in the journal Animal - Open Space.</p>
A Practical Tool-Chain for the Development of Coordination Scenarios - Graphical Modeler, DSL, Code Generators and Automaton-Based Simulator
<p>The Peer Model is a modeling tool for coordination based on blackboard-based collaboration. </p> <p>The tool-chain consists of a modeler, translator and simulator.</p> <p>Its goal is to help developers of distributed and concurrent coordination software better understand algorithms and identify deficiencies from the beginning.</p> <p><br> </p>
Development and validation of statistical shape models of the primary functional bone segments of the foot.
<p>This dataset comprises manually segmented three-dimensional point clouds (.STL) of magnetic resonance images of the primary functional segments of the foot - first metatarsal, midfoot (second-to-fifth metatarsals, cuneiforms, cuboid, and navicular), calcaneus, and talus. These data were used to create statistical shape models of the foot bones, utilising the GIAS2 toolbox (https://pypi.org/project/gias2/).</p>
Interviews for New Business Models for Pharmaceutical Innovation and Access to Medicines - Case Study of the Oral Cholera Vaccine Development
<p>These supplementary materials represent the partial dataset in the form of semi-structured interviews, collected and analyzed in the research article "The 30-year evolution of oral cholera vaccines: A case study of a collaborative network alternative innovation model". This article is one of the outcomes of the "New Business Models for Pharmaceutical Innovation and Global Access to Medicines" research project, conducted at the Global Health Center, within the Geneva Graduate Institute. The dataset contains 8/16 interviews collected and used in this article, which are published with the informed consent of the interviewees.</p>
RDF version of the data from Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).
<p>This is an RDFied version of the dataset published by Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1016/j.impact.2021.100308">https://doi.org/10.1016/j.impact.2021.100308</a></p> <p>The Original publication authors: Anastasios G. Papadiamantis, Antreas Afantitis, Andreas Tsoumanis, Eugenia Valsami-Jones, Iseult Lynch, Georgia Melagraki</p>
Identification of Genes Regulating Dexamethasone Resistance and Prognostic Model Development in Acute Lymphoblastic Leukemia
<p>This study investigates the mechanisms of dexamethasone resistance in acute lymphoblastic leukemia (ALL) and presents a prognostic model to predict patient outcomes and immunotherapy responses. By analyzing gene expression data, we identified autophagy-related genes associated with dexamethasone resistance, particularly focusing on STK38L’s role in modulating autophagy via ULK1. Our results reveal that high STK38L expression enhances dexamethasone resistance by promoting autophagy markers LC3II/LC3I and beclin-1. This study provides valuable insights into the molecular basis of dexamethasone resistance and highlights STK38L as a potential biomarker and therapeutic target for improving ALL treatment strategies.</p>
Development and analysis of entropy stable no-slip wall boundary conditions for the Eulerian model for viscous and heat conducting compressible flows
<p>The database used in the submission of "Development and analysis of entropy stable no-slip wall boundary conditions implementation of the Eulerian model for viscous and heat conducting compressible flows."</p> <p>Abstract: Nonlinear entropy stability analysis is used to derive entropy stable no-slip wall boundary conditions for the Eulerian model proposed by Svärd ( <em>Physica A: Statistical Mechanics and its Applications, 2018 </em>). and its spatial discretization based on entropy stable collocated discontinuous Galerkin operators with the summation-by-parts property for unstructured grids. A set of viscous test cases of increasing complexity are simulated using both the Eulerian and the classic compressible Navier–Stokes models. The numerical results obtained with the two models are compared, and differences and similarities are then highlighted.</p>
A Synthetic Hyperspectral Dataset for Development and Validation of Phytoplankton Size Class Retrieval Models
<p><strong>A Synthetic Hyperspectral Dataset for Development and Validation of Phytoplankton Size Class Retrieval Models.</strong></p> <p>Please refer to the following scientific paper for a description of the dataset.</p> <blockquote> <p>Holtrop, T.; Van Der Woerd, H.J. (accepted) HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland Waters. <em>Remote Sens. </em><strong>2021</strong>, 13, 0.</p> </blockquote>
Data base of cycles 1 and 2 of biometric variables of fuzzy model for assessing the development of the radish crop
<p>This data represent the fuzzy model developed of a Rule-Based System (RBS) to evaluation the development of the radish crop in two production cycles, for the irrigation depth at 100% of evapotranspiration. This RBS represents the function <span class="math-tex">\(f:\mathbb{R}\rightarrow\mathbb{R}^{10}\)</span>, where the domain is represented by the Days After Sowing (DAS), and counterdomain is represented by the ten biometric variables, denominated: Number of Leaves (NL), Root Length (RL), Bulb Diameter (BD), Bulb Length (BL), Green Root Weight (GRW), Green Leaf Weight (GLW), Green Bulb Weight (GBW), Dry Root Weight (DRW), Dry Leaf Weight (DLW). </p>
Mainshock+aftershock M4.95+ seismicity forecasts derived from the Regional Earthquake Likelihood Models (RELM) and the multiplicative hybrid earthquake models developed by Rhoades et al. (2014)
<p>Contains six mainshock+aftershock seismicity forecasts developed by the Working Group of the Regional Earthquake Likelihood Models (RELM) experiment, sixteen multiplicative hybrid forecasts created by Rhoades et al. (2014), and the 2011-2020 M4.95+ ANSS earthquake catalog for California. Six additional forecast files are included to properly conduct the comparative tests implemented in the Collaboratory for the Study of Earthquake Predictability (CSEP) testing centre.</p> <p>Forecasts are stored in tab separated value files with the following fields (the first row of data is shown as an example):</p> <pre>LON_0 LON_1 LAT_0 LAT_1 DEPTH_0 DEPTH_1 MAG_0 MAG_1 RATE FLAG -125.4 -125.3 40.1 40.2 0.0 30.0 4.95 5.05 5.8499099999999998e-04 1 </pre> <p>Forecast are described in detail by the following publications:</p> <p>Bird, P., and Z. Liu (2007). Seismic Hazard Inferred from Tectonics: California. Seismological Research Letters, 78(1):37-48.</p> <p>Ebel, J. E., D. W. Chambers, A. L. Kafka, and J. A. Baglivo (2007). Non-Poissonian Earthquake Clustering and the Hidden Markov Model as Bases for Earthquake Forecasting in California. Seismological Research Letters, 78(1): 57-65.</p> <p>Helmstetter, A., Y. Y. Kagan, and D. D. Jackson (2007). High-resolution Time-independent Grid-based Forecast for M >= 5 Earthquakes in California. Seismological Research Letters, 78(1): 78-86.</p> <p>Holliday, J., Chen, C., Tiampo, K., Rundle, J., Turcotte, D., and Donnellan, A. (2007). A RELM earthquake forecast based on pattern informatics. Seismological Research Letters, 78(1):87–93.</p> <p>Kagan, Y. Y., D. D. Jackson, and Y. Rong (2007). A Testable Five-Year Forecast of Moderate and Large Earthquakes in Southern California Based on Smoothed Seismicity. Seismological Research Letters, 78(1): 94-98.</p> <p>Rhoades, D.A., Gerstenberger, M.C., Christophersen, A., Zechar, J.D., Schorlemmer, D., Werner, M.J. and Jordan, T.H., 2014. Regional earthquake likelihood models II: Information gains of multiplicative hybrids. Bulletin of the Seismological Society of America, 104(6):3072-3083.</p> <p>Shen, Z.-K., D. D. Jackson, and Y. Y. Kagan (2007). Implications of Geodetic Strain Rate for Future Earthquakes, with a Five-Year Forecast of M5 Earthquakes in Southern California. Seismological Research Letters, 78(1):116-120.</p> <p>Ward, S. (2007). Methods for evaluating earthquake potential and likelihood in and around California. Seismological Research Letters, 78(1):121–133.</p> <p>Wiemer, S. and Schorlemmer, D. (2007). ALM: An asperity-based likelihood model for California. Seismological Research Letters, 78(1):134–140.</p>
Pellet-based fused deposition modeling for the development of soft compliant robotic grippers with integrated
<p>Fused deposition modeling (FDM) has some advantages compared to other additive manufacturing techniques, such as the in situ integration of functional components, like sensors, and recyclability of parts. However, conventional filament-based FDM techniques are limited to thermoplastic elastomers with a Shore hardness above 70 A, thus it has marginal compatibility with soft robotic structures. Due to recently emerging pellet-based FDM printer technology, the fabrication of soft grippers with low Shore hardness has become possible. In this study, styrene based thermoplastic elastomers (TPS) were used to print elastic strips and soft gripper structures down to a Shore hardness of 25 A with an integrated strain sensing element (piezoresistive sensor). Printing on a soft rather than rigid substrate affects the integration of the printed thread on the substrate, because of the softness and relaxation, during the printing softness. It was seen that integrating the sensing element on a substrate with higher Shore hardness decreased the elongation at the point of fracture and the sensitivity of the sensing element. A soft compliant gripper structure with an integrated sensing layer was printed with the TPS-based elastomers successfully, and even due to the complex deformation of the compliant gripper structure, several positions could be detected successfully. Opened and closed position of the gripper, as well as, size recognition of spools of different sizes could be monitored by the piezoresistive printed sensor layer. The most sensitive sensing performance was obtained with the TPS of the lower Shore hardness (25 A), as the value of relative change in resistance was 1, followed by the gripper of Shore hardness 65 A and a relative change in resistance of 0.51. With this study, we demonstrated that pellet-based FDM printers can be used, to print potential soft robotic structures with in-situ integrated sensor structures.</p>
Development of a machine learning model for river bedload - Data, Model, and Scripts
<p>This repository for “Development of a machine learning model for river bedload” Hosseiny et. Al (in review at Earth Surface Dynamics) contains the following assets. These assets may need to be modified for your purposes. You are responsible for inspecting these assets and making adjustments as necessary.</p> <p>Assets:</p> <p>1) The trained ANN model described in Hosseiny et al. (in review) as a .zip file named ‘Hosseiny_et_al_trained_ANN.zip’. This contains a folder (BEDLOAD_MODEL_FINAL) which contains the trained ANN Model (saved_model.pb) and associated information related to the input variables and variable weights.</p> <p>2) An accompanying Jupyter notebook named “bedload_ann_example.ipynb” that provides a step-by-step guide for implemented the trained ANN model (Asset 1).</p> <p>3) A .xls file named “Hosseiny et al_Supplemental_Data_Tables.xlsx” which provides the original observations that the model was trained and tested on, the summary statistics of the original input data as a compilation and for individual sites, the model errors associated with training and validation steps, the bedload calculations from the four uncalibrated existing bedload transport models described in the original study and for the ANN for the test data population, associated summary statistics with model output, and additional site-specific calculations of model error.</p>
Dataset for the calibration of the photosynthetic unit (PSU)-model developed at KIT
<p>The presented dataset was used to develop and calibrate (estimate the model parameters) a photosynthetic unit (PSU)-model. The PSU-model and the data were generated at the Institute of Process Engineering in Life Sciences – Bioprocess Engineering of the KIT under the supervision of Prof. Dr.-Ing. Clemens Posten. Please use this data to familiarize yourself with the PSU-model and as an example dataset. If you want to use your own data in this context, organize it like the sample data listed here. As an alternative, you should adapt the model scripts to your data accordingly.</p> <p>Date of data collection: The data were generated as a part of the author’s PhD program between 2018-2022 at Karlsruhe Institute of Technology (KIT).</p>
Dataset for the article "Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes"
<p>This dataset include the data needed to create the socio-hydrological model to simulate human evacuation processes via a transportation network before a flood hits the residential area. Source code, in JAVA, for generating households in the agent-based model are also provided. </p>
Occurrence records used to develop a climatic suitability model for emerald ash borer in DDRP
<p>Presence records used to calibrate and validate a climatic suitability model for emerald ash borer in the DDRP platform (Degree-Days, Risk, and Phenological event mapping) (Barker et al. 2023). The first sheet ("Records") of the Excel file provides the range (native or invaded), continent, country, state or province, locality, latitude, and longitude of origin for each record. The "Coords_est" column indicates whether the coordinates were estimated from city- or county-level information (1 = yes, 0 = no). The year in which the record was collected is provided if known. The second sheet of the Excel file ("References") provides a list of references for each record source.</p>
Model output from CAABA/MECCA study "Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)"
<p>This dataset includes the main data obtained during the study "Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)" (DOI:10.5194/gmd-2023-102). The updated model code can be found at zenodo.org (DOI:10.5281/zenodo.7944174). The data can be used to replicate the results shown in the manuscript. Contained are results produced by the updated CAABA/MECCA (version 4.7.0) and reference data from CAABA/MECCA version 4.5.5. In version 4.7.0, new biogenic and anthropogenic species are introduced to the model (limonene and long-chained alkanes) with refined multiphase chemistry, while new reaction pathways are added for existing compounds (isoprene, benzene and IEPOX). The output is generated to evaluate model results in terms of temperature- and NOx-dependency.</p>
agroBRIDGES EIP-AGRI Practice Abstracts - Regional insights and business models for SFSC development
<p>The present dataset completed the collection of the 25 regional practice abstracts developed in the framework of the agroBRIDGES project, using the EIP-AGRI common format. The aim is to share project outputs so other actors of the agri-food sector can leverage on them and to see the main added value and benefits met by the end-users if the generated knowledge is implemented. The Practice Abstracts are also published on the EIP-AGRI website and available at this <strong><a href="https://ec.europa.eu/eip/agriculture/en/find-connect/projects/building-bridges-between-consumers-and-producers">link</a>. </strong></p> <p>The current (second) version of the dataset builds upon the previous set of Practice Abstracts generated in each of the 12 European focal countries of the project to share the project's knowledge and insights gained from co-creation, data collection, communication, toolbox validation and event organisation activities.</p> <p>These activities aim at promoting the development of Short Food Supply Chains, while motivating the local network of agri-food actors in the process (multi-actor approach). At least one Practice Abstract has been generated per Beacon Region (i.e. focal region of the agroBRIDGES project). The Practice Abstracts aim to support proactitioners in replicating our approaches and methodologies.</p> <p>2 Practice Abstracts have been elaborated per country (23 in total):</p> <ul> <li>Ireland</li> <li>Denmark</li> <li>Italy (Lazio & Piemonte regions)</li> <li>Greece (Central Macedonia region)</li> <li>Spain (Andalusia region)</li> <li>France (Pays de Loire, Auvergne and Bretagne regions)</li> <li>Poland</li> <li>Latvia</li> <li>Lithuania</li> <li>Turkey</li> <li>Finland</li> <li>Netherlands</li> </ul> <p>Additionally, 1 Practice Abstract was developed to present 5 business model categories of SFSC analysed in the context of agroBRIDGES. </p>
USGSG16AP00094: Developing a seismic velocity model of the central valley, northern California: model SSJD2016
<p>Seismic velocity model SSJD2016 uses earthquake travel-time, ambient noise group velocity and gravity data to update Thurber NC2009, for northern California.</p>
ScienceDex guides
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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.