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1,855 results for “Framework”
Supplementary material 1 from: Eitzel M.V (2021) A modeler's manifesto: Synthesizing modeling best practices with social science frameworks to support critical approaches to data science. Research Ideas and Outcomes 7: e71553. https://doi.org/10.3897/rio.7.e71553
Modeler's Manifesto: Self-Situating Appendix
Figure 1 from: Eitzel M.V (2021) A modeler's manifesto: Synthesizing modeling best practices with social science frameworks to support critical approaches to data science. Research Ideas and Outcomes 7: e71553. https://doi.org/10.3897/rio.7.e71553
Figure 1 Workflow diagram for manifesto practices, showing which project stages may benefit from which practices. Interdisciplinary fluency, engaging with community-based modeling, and paying attention to power dynamics as well as impacts and implications are all important at all stages of modeling work. Epistemic consistency is important throughout model development (the three middle steps of model choice, construction, and description) and communication, while triangulation and mixed methods contribute largely to model development. The data biography is most important in the model description stage, though one may need to keep a journal and track details of the model development process in order to create the data biography. Treating uncertainty as openness is most important in model communication and application; however, this could feed back into iterative model development steps as well, or one could design models to aid in treating uncertainty as openness.
An ensemble deep learning framework to refine large deletions in linked-reads
<p>An ensemble deep learning framework to refine large deletions in linked-reads</p>
Research data for "Visualization and Quantification of Geometric Diversity in Metal-Organic Frameworks"
<p>This dataset supports the paper: "Visualization and Quantification of Geometric Diversity in Metal-Organic Frameworks".</p> <p>The coarse-grained scaled and unscaled structures are provided here in CIF format. The code introduced in this work is subject to continuing development (and can be found at: https://github.com/tcnicholas/coarse-graining), therefore we include here the version of the code used for this paper alongside the Python analysis scripts.</p> <p>The original unprocessed CIFs were extracted from the Cambridge Structural Database (CSD).</p>
Data and Code Supporting Hagy et al. Quantifying coastal ecosystem condition and a trophic state index with a Bayesian analytical framework
<p>The code includes several R markdown files and an associated file containing R functions. The main body of code <em>coastal_TSI_ts.Rmd</em> loads the function file located in the functions folder. Raw data is included as .csv files in the raw directory and various data sets created as intermediate products are in the data folder. Variable definitions are included in a data dictionary.</p> <p>The code has been used to generate the analysis reported in</p> <p>Hagy, JD, B. Kreakie, M. Pelletier, F. Nojavan, J. Kiddon, and A. Oczkowski. Quantifying coastal ecosystem condition and a trophic state index with a Bayesian analytical framework</p> <p>This file contains a zip of a github repository, https://github.com/USEPA/-cTSI</p>
Codes and datasets associated with the paper "Simulating an extreme over-the-horizon optical propagation event over Lake Michigan using a coupled mesoscale modeling and ray tracing framework"
<p>Here, you will find some of the codes, images, and datasets utilized in the article: </p> <p>Basu (2017). "Simulating an extreme over-the-horizon optical propagation event over Lake Michigan using a coupled mesoscale modeling and ray tracing framework", Optical Engineering, 56(7), 071505 (https://doi.org/10.1117/1.OE.56.7.071505)</p> <p>WRF codes: namelist.wps, namelist.input, myoutfields.txt</p> <p>NCL codes: d02_terrain.ncl, wrf_SurfaceASTD_d02.ncl</p> <p>RADAR loop: KGRR.gif</p> <p>MATLAB codes: Plot_Buoy.m</p> <p>Note: buoy datasets are available publicly from https://www.ndbc.noaa.gov/ </p>
Input Files and Results for Benchmarking the iMD-VR Framework.
<p>Sets of input files and result for benchmarking the iMD-VR framework presented in the thesis Accelerated Sampling of High Dimensional Molecular Systems by Mike O'Connor. </p> <p>The benchmarks folder contains the input files and results of the benchmarks for testing the scaling of the NarupaXR framework (in 2018).</p> <p>The interactions_test folder contains the input files and results of the benchmarks testing the group interaction potentials and velocity reinitialisation procedures with the knotted protein MJ0366.</p> <p> </p>
Table S2. Enhancer-gene correlation framework
<p>Outcome of all enhancer-gene correlations with FDR < 0.05</p>
A predictive group-contribution framework for the thermodynamic modelling of CO2 absorption in cyclic amines, alkyl polyamines, alkanolamines and phase-change amines: new data and SAFT- gamma Mie parameters.FPE 2022
<p>All data in the figures in the publication. </p>
A General Framework for Spatial Capture-Recapture with Unknown Identities
<p>Data to accompany examples in the manuscript.</p>
Smartphone-Based Incentive Framework for Dynamic Network-Level Traffic Congestion Management Project H3
<p>Task 2 numerical experiment results</p>
Supporting data for 'Simple framework for systematic high-fidelity gate operations'
<p>The data must not be reused in other studies or publications without approval of the authors.</p>
Resource Description Framework (RDF) Modeling of Named Entity Co-occurrences Derived from Biomedical Literature in the PubChemRDF
<p>This is the dataset used for the publication of "Resource Description Framework (RDF) Modeling of Named Entity Co-occurrences Derived from Biomedical Literature in the PubChemRDF". </p>
Human Reference Atlas-Vasculature Common Coordinate Framework (HRA-VCCF)
<p>The Human Reference Atlas-Vasculature Common Coordinate Framework (HRA-VCCF) is a computer-readable and comprehensive database of the adult human blood vasculature.</p>
Supplementary material 6 from: Herrera-Mesías F, Ep Jarboui IK, Weigand AM (2022) A metabarcoding framework for wild bee assessment in Luxembourg. Journal of Hymenoptera Research 94: 215-246. https://doi.org/10.3897/jhr.94.84617
Mock community metabarcoding results
Supplementary material 2 from: Herrera-Mesías F, Ep Jarboui IK, Weigand AM (2022) A metabarcoding framework for wild bee assessment in Luxembourg. Journal of Hymenoptera Research 94: 215-246. https://doi.org/10.3897/jhr.94.84617
Mean wild bee genus penalty scores sorted by primer pair
Supplementary material 4 from: Herrera-Mesías F, Ep Jarboui IK, Weigand AM (2022) A metabarcoding framework for wild bee assessment in Luxembourg. Journal of Hymenoptera Research 94: 215-246. https://doi.org/10.3897/jhr.94.84617
Tagged primer combinations used in the mock community experiment
Supplementary material 9 from: Herrera-Mesías F, Ep Jarboui IK, Weigand AM (2022) A metabarcoding framework for wild bee assessment in Luxembourg. Journal of Hymenoptera Research 94: 215-246. https://doi.org/10.3897/jhr.94.84617
Primer pair amplification success rates based on Linnaean species
Supplementary material 5 from: Herrera-Mesías F, Ep Jarboui IK, Weigand AM (2022) A metabarcoding framework for wild bee assessment in Luxembourg. Journal of Hymenoptera Research 94: 215-246. https://doi.org/10.3897/jhr.94.84617
Overiew of MOTU data per mock community
Supplementary material 8 from: Herrera-Mesías F, Ep Jarboui IK, Weigand AM (2022) A metabarcoding framework for wild bee assessment in Luxembourg. Journal of Hymenoptera Research 94: 215-246. https://doi.org/10.3897/jhr.94.84617
In silico primer performance evaluation using PrimerMiner with MOTU data sorted by wild bee genus
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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.