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57 results for “additive models”
Input data and modelling files for a model of the Finnish energy system with focus on cascade hydropower and the addition of a hydrogen storage system realised in Backbone
<p>The files show the input data and modelling files used for the publication "Cascade hydropower integration in a techno-economic power system model: A study of Finnish hydropower plants" (Kiehle et al., 2025 - submitted). The paper's <a title="Preprint on SSRN" href="https://dx.doi.org/10.2139/ssrn.4971685" target="_blank" rel="noopener">preprint</a> is available. A model of the Finnish energy system in 2022 was built in the techno-economic modelling framework Backbone (available on GitLab: https://gitlab.vtt.fi/backbone/backbone). The focus was on implementing cascading hydropower plants in a power system model, including individual reservoirs, generation and spillage capacities. </p> <p>"ModellingFiles_Debug" are GAMS-based data that can be used to run the scenario in Backbone or display the results. "ModellingResults" are gdx files that purely list the results. Those are also presented in more detail in the scientific paper. The Excel files present the input data used for modelling and can also be used to run the model. </p>
Additions to AGORA2 made for Shaaban et al, "Personalized modeling of gut microbiome metabolism throughout the first year of life"
<p>This dataset contains expansions made to AGORA2 (https://www.nature.com/articles/s41587-022-01628-0) and published in Shaaban et al, "Personalized modeling of gut microbiome metabolism throughout the first year of life", in press.</p> <p>Included are:</p> <ul> <li>289 additional genome-scale reconstructions built for genomes not included in AGORA2</li> <li>250 genome-scale reconstructions from AGORA2 endapnded with human milk oligosaccharide degradation pathways</li> </ul> <p>In this version, slight updates have been made to the 289 additional genome-scale reconstructions based on additional experimental data.</p>
Data for "International Transport costs: New Findings from modeling additive costs"
<p>Downloading the data will provide you with a .zip file.</p> <p>These data are US imports from 1974 to 2020, by partner, transport mode and product at the 5 digit level in the "Hummels" data and by partner, transport mode, district of entry, district of unlading and product at the 10 digit level for the rest of the data.</p> <p>All the data originally come from the Census Bureau "Foreign Trade" (see https://www.census.gov/foreign-trade/index.html) and belong to the public domain.</p> <p>"Hummels_JEP _data" was downloaded from David Hummels’s website (https://www.krannert.purdue.edu/faculty/hummelsd/research/jep/data.html -- Hummels, David, "Transportation Costs and International Trade in the Second Era of Globalization", <em>Journal of Economic Perspectives</em>, Vol 21, No 3, pp 131-154. It covers 1974 to 2004.</p> <p>The other main files were bought directly from the Census Bureau ("Annual Merchandise Trade files"). They cover 1997-1999 and 2002-2020.</p> <p>Various files are included for the conversion of country codes (dist_cepii.dta, countrycodes_use.txt), product codes (HS2002_SITC2.txt) and to associate quantity units with hts codes (hts... and ..._hts_...).</p>
Datasets and Code for "A Gaussian process model-guided surface polishing process in additive manufacturing"
<p>These are the datasets and computer code for reproducing the results in Jin, Iquebal, Bukkapatnam, Gaynor, and Ding, 2020, “A Gaussian process model-guided surface polishing process in additive manufacturing.” <em>ASME Transactions, Journal of Manufacturing Science and Engineering</em>, Vol. 142(1), pp. 011003.1–011003.12.</p>
Documenting And Assessing Open Innovation: Co-creation Of An Open Data Model For Surgical Training (Additional materials, tables 2 & 3)
<p>Challenge competitions have recently resurged for promoting open innovation in areas where markets fail to provide incentives, such as the Sustainable Development Goals (SDGs). Challenges call for the general public to contribute novel solutions to a well-defined problem, in exchange for prizes, credentials and the promise of further development of selected solutions. The aim of this paper is to report on the development of an open and collaborative data model to document and evaluate innovations in the context of a challenge competition, while also being compatible with the work of other open source communities to validate and improve them. By reusing open documentation standards and embedding them into a semantic collaborative platform, the model aimed to be flexible enough to respond to the evaluation needs of the project organisers and self-assessment for participants. We expect our experience provides insights on the potential of semantic, collaborative platforms and standards for increasing the impact of innovations towards the SDGs.</p> <p>The developer team defined the goal and scope of the ontology in collaboration with the GSTC organisers. This was done by agreeing on scenarios where the ontology will be used and establishing competency questions that the ontology has to be able to respond to. Table 2 describes the four motivating scenarios, including actors involved, requirements, sequence of actions and main problems identified. Table 3 details the competency questions for each scenario.</p>
Additional Figures for winning models for sample in A Comparative L-dwarf Sample Exploring the Interplay Between Atmospheric Assumptions and Data Properties
<p>Additional Figures for winning models for sample in <em>A Comparative L-dwarf Sample Exploring the Interplay Between Atmospheric Assumptions and Data Properties (<a href="https://arxiv.org/pdf/2209.02754.pdf">https://arxiv.org/pdf/2209.02754.pdf</a>).</em></p> <p>Model naming key: NC = cloud-free, d2_89 = power-law deck cloud</p> <p>SDSS J1416+1348A: Winning model: power-law deck cloud</p> <p>Spectral Type Comparison J1526+2043 Winning model: Cloud-free</p> <p>Temperature Comparisons</p> <p>J1539-0520 Winning model: Power-law deck cloud and cloud-free tied.</p> <p>J0539-0059 Winning model: Power-law deck cloud and cloud-free tied. </p> <p><br> </p>
Additional raw video and pose estimation data of top view mouse behavior recordings (marble burying test, light-dark box, fear conditioning box) of acute and chronic stress models
<p>This repository contains raw data for 296 different behavioral recordings of mice (marble burying test, light-dark box, fear conditioning box). These include top view raw video .mp4 files (Videos.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from multiple different experiments. The METADATA.csv or METADATA.xlsx files contain all grouping variables and help linking the pose estimation files (located in multiple subfolders of /data) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how this data has be used by us.</p>
Data for "Integrated ab initio modelling of atomic order and magnetic anisotropy for rare-earth-free magnet design: effects of alloying additions in L1$_0$ FeNi."
<p>Data for "Integrated ab initio modelling of atomic order and magnetic anisotropy for rare-earth-free magnet design: effects of alloying additions in L1$_0$ FeNi", published in npj Comput. Mater. <strong>10</strong> 272 (2024).</p> <p>Version 2 contains additional results relating to Ni-rich systems.</p> <p>Version 3 contains data relating to vibrational considerations for the A1-L1$_0$ transition in equiatomic FeNi.</p> <p>Version 4 contains date pertaining to the Curie temperatures of the disordered, partially ordered, and fully ordered alloys considered in this work.</p>
Primary collected data for modelling the additive MAR/R process by means of the PBF-LB process based on the example of tool steel 1.2709 powder
<p>For the evaluation of a MAR/R process, not only process-, material- and demonstrator-specific correlations and data must be combined. In addition to secondary data (e.g. databases, publications, etc.), primary data (e.g. process times, volume flows, etc.) must also be collected for the specific application.</p> <p>The attached table shows the primary data to be collected for the cradle-to-gate process depending on the process phases and steps. This data is used as support for ecological as well as economic process and component evaluations.</p>
STL files: Modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing
<p>STL files for paper titled modeling and design of heterogeneous hierarchical bioinspired spider web structures using deep learning and additive manufacturing</p>
Dynamics of leaching of POPs and additives from plastic in a Procellariiform gastric model: Diet and polymer dependent effects and implications for long-term exposure
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Data from: Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model
We report a genomic selection (GS) study of growth and wood quality traits in an outbred F2 hybrid Eucalyptus population (n=768) using high-density single-nucleotide polymorphism (SNP) genotyping. Going beyond previous reports in forest trees, models were developed for different selection targets, namely, families, individuals within families and individuals across the entire population using a genomic model including dominance. To provide a more breeder-intelligible assessment of the performance of GS we calculated the expected response as the percentage gain over the population average expected genetic value (EGV) for different proportions of genomically selected individuals, using a rigorous cross-validation (CV) scheme that removed relatedness between training and validation sets. Predictive abilities (PAs) were 0.40–0.57 for individual selection and 0.56–0.75 for family selection. PAs under an additive+dominance model improved predictions by 5 to 14% for growth depending on the selection target, but no improvement was seen for wood traits. The good performance of GS with no relatedness in CV suggested that our average SNP density (~25 kb) captured some short-range linkage disequilibrium. Truncation GS successfully selected individuals with an average EGV significantly higher than the population average. Response to GS on a per year basis was ~100% more efficient than by phenotypic selection and more so with higher selection intensities. These results contribute further experimental data supporting the positive prospects of GS in forest trees. Because generation times are long, traits are complex and costs of DNA genotyping are plummeting, genomic prediction has good perspectives of adoption in tree breeding practice.
Dataset and Additional Information for the paper A LINEAR-ALGEBRAIC MODEL FOR ESTIMATING ANTI-LEARNING WHEN A DECISION TREE SOLVES THE PARITY BIT PROBLEM, by ALEXEI LISITSA and ALEXEI VERNITSKI (submitted)
<p>This upload contains a dataset and additional information for the paper A LINEAR-ALGEBRAIC MODEL FOR ESTIMATING<br> ANTI-LEARNING WHEN A DECISION TREE SOLVES THE PARITY BIT PROBLEM, by ALEXEI LISITSA and ALEXEI VERNITSKI (submitted) </p>
Representation of model error in convective-scale data assimilation: additive noise, relaxation methods and combinations
<p>ModelError_1_dpsdt.tar.xz for Fig. 11<br> <br> ModelError_1_innovstat.tar.xz for Fig. 4, 10 and 13<br> <br> ModelError_1_KESpectrum.tar.xz for Fig. 1<br> <br> ModelError_1_pre_veri.tar.xz for Fig. 8 and 9<br> <br> ModelError_1_precipobs_tau.tar.xz for Fig. 2<br> <br> ModelError_1_refl_veri.tar.xz for Fig. 6, 12 and 14</p> <p>For plotting, MATLAB (R2017b) and python are used</p> <p> </p>
Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distributions
<p>Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSF), or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from Golden Eagles (Aquila chrysaetos) from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k-fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.</p>
Monte Carlo Simulations results for estimating an offshore structure fatigue life with a Fracture Mechanics based crack growth model, after additional information was considered through Bayesian inference at t=13 years
<p>Monte Carlo Simulations results for estimating an offshore structure fatigue life with a Fracture Mechanics based crack growth model, after additional information was considered through Bayesian inference at t=13 years</p>
Estradiol Enhances the Development of Addition-Like Features in a Female Rat Model of Opioid Use Disorder
<p>This dataset indicates that, as with findings with psychostimulants and alcohol, estradiol enhances vulnerability in females to developing opioid addiction-like features and serious opioid-related health complications in a rat model of opioid use disorder.</p>
3D-printed Bone Models in Addition to CT Imaging for Intra-articular Fracture Repair
ClinicalTrials.gov study NCT04748016. IPD Sharing: YES. Countries: 1. Publications: 18.
Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distributions
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Data from: Modeling additive and non-additive effects in a hybrid population using genome-wide genotyping: prediction accuracy implications
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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.