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1,197 results for “flexibility”
Post-consumer flexible packaging characterization
<p>Dataset on post-consumer waste flexible packaging </p> <p>Analysis of the composition made with FTIR spectrometer Antaris II </p> <p>200 Samples characterized</p> <p>Waste coming for French Material Recovery Facilities (Yellow bins) </p> <p> </p>
Danish case study simulation results (consumption, offered & accepted flexibility, revenues & expenses)
<p>This file contains results of the simulations of the Danish case study generated during Horizon 2020 MAGNITUDE project. The case study considers the provision of domestic hot water from 350 heat pumps installed in multi-storey apartment buildings. Heat pumps receive energy from electrical grid and use heat from ultra-low temperature district heating (ULTDH) as heat source. The overall system is referred to as Multi-Energy System (MES). MES can provide flexibility to either intra-day (ID) market or redispatch (RE) services to local distribution system operator. Results show how much flexibility was offered and activated, and the resulting consumption and production profiles due to that. In addition, the revenues and expenses based on the historical prices for Denmark in 2018 had been calculated.</p>
Backyard Beetles and Pollinators Dataset - EREN/NEON Flexible Learning Project
<p>This dataset comes from the EREN-NEON flexible learning project 'Backyard Beetles and Pollinators.' It can be used for teaching field, computational, or hybrid courses. The dataset is standardized, visual observations of insect plant visitors, identified to standard functional groups, to indirectly assess pollination and construct plant-pollinator interaction networks. Insects were identified to morphospecies in the field using reference images. We also collected information about the flowers the insects were observed on - including functional type information about the color, size, and type of flower - as well as cover. This information is part of an ongoing course-based undergraduate research project to both teach about plants, insects, and functional biodiversity in a flexible and inclusive way - while collaboratively assessing interaction networks across landscapes and time. </p> <p>To use the flexible lesson materials or join the collaboration, get more information here: https://erenweb.org/eren-neon-flexible-learning-projects/ Or contact the project lead, Dr. Stack Whitney, directly at kxwsbi [at] RIT [dot] edu. </p>
Dataset of 20 energy prosumers with flexibility data, distributed generation and energy storage
<p>The dataset has 20 prosumers, each with three appliances to provide flexibility for DR events, two PV generation resources, and an energy storage system. The values represent a day using 15 minutes reading periods. All the values are expressed in W, and the matrixes were created as [ time_period x info].</p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
Raw MS data for "Ligand-specific changes in conformational flexibility mediate long-range allostery in the lac repressor"
<p>These are the raw HDX/MS data for our paper: "Ligand-specific changes in conformational flexibility mediate long-range allostery in the lac repressor."</p>
Benne: A Modular Data Stream Clustering Algorithm with Flexible Design Choices
<p>All of the source dataset with preprocessed format [id features class] that have been used for evaluation in the paper.</p>
Dataset to Study Grid-Secure Use of Distributed Flexibility in Sequential DSO-TSO Markets
<p>We publish the dataset used to study Grid-Secure Use of Distributed Flexibility in Sequential DSO-TSO Markets (as part of chapter 7 of deliverable D3.3 of the OneNet project).</p> <p>The dataset is composed by an interconnected system consisting of the IEEE 14-bus (TN) transmission network connected to two distribution networks: the Matpower systems 69-bus (DN_69), and 141-bus (DN_141). All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Injections and loads of the nodes are adapted to create an anticipated imbalance in the interconnected system, resolved by flexibility. In addition, the lines’ upper limits are adjusted to create anticipated congestion in the networks. The interconnected system is fully represented in "Network_case_A_B_C.xlsx" (upward balancing need) and "Network_case_D.xlsx" (downward balancing need).<br>Upward and downward flexibility bids are randomly generated and allocated to the nodes. </p> <p>7 bids lists are available in this dataset. </p> <p>Source of the systems' topology:</p> <p>[1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010.</p> <p>Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate Grid-Secure Use of Distributed Flexibility in Sequential DSO-TSO Markets. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.</p>
Code and data: Exploring congruent diversification histories with flexibility and parsimony
<p>This repository contains the code and data for the article "Exploring congruent diversification histories with flexibility and parsimony" (abstract bellow).</p> <p>Data :</p> <ul> <li><strong>4705sp_mammal-time.tree</strong>: Species-level calibrated mammalian phylogeny from <em>Alvarez-Carretero et al. </em>(<a href="https://doi.org/10.6084/m9.figshare.14885691">https://doi.org/10.6084/m9.figshare.14885691</a>)</li> <li><strong>mammals_samplingfraction.csv</strong> : Clade-specific sampling fractions from <em>Quintero et al.</em>(<a href="https://www.biorxiv.org/content/10.1101/2022.08.09.503355v1.full">https://www.biorxiv.org/content/10.1101/2022.08.09.503355v1.full</a>).</li> </ul> <p>Code :</p> <ul> <li><strong>CRABS-v1.1.0.9004.zip</strong>: Archived version of the CRABS package with our extension.</li> <li><strong>Mammalian_rates_EBD_HSMRF.rev</strong>: <em>Rev</em> script for the mammalian diversification analysis in RevBayes with regularized priors on diversification rates.</li> <li><strong>Mammalian_rates_EBD_independent.rev</strong>: <em>Rev</em> script for the mammalian diversification analysis in RevBayes with independent diversification rates at each interval.</li> <li><strong>Mammals_proccess_RevBayes_outputs.Rmd</strong>: R notebook for processing the outputs from the RevBayes mammalian diversification analysis, plotting the rates through time, and saving the median trajectories used for further analyses.</li> <li><strong>Exploring_congruent_diversification_histories_with_flexibility_and_parsimony.Rmd</strong>: R notebook for comparing the initial CRABS features and our new extensions. It enables replicating the figures in the article.</li> </ul> <p>Outputs :</p> <ul> <li><strong>output_inferredIntervals_fixedRhp_HSMRF.zip</strong> & <strong>output_inferredIntervals_fixedRhp_independent.zip</strong>: The raw traces from the RevBayes analysis, and the resulting median rate trajectories that are used to construct the congruence class illustrated in the article.</li> </ul> <p><br> Abstract</p> <ol> <li>Using phylogenies of present-day species to estimate diversification rate trajectories -- speciation and extinction rates over time -- is a challenging task due to non-identifiability issues. Given a phylogeny, there exists an infinite set of trajectories that result in the same likelihood; this set has been coined a congruence class. Previous work has developed approaches for sampling trajectories within a given congruence class, with the aim to assess the extent to which congruent scenarios can vary from one another. Based on this sampling approach, it has been suggested that rapid changes in speciation or extinction rates are conserved across the class. Reaching such conclusions requires to sample the broadest possible set of distinct trajectories.</li> <li>We introduce a new method for exploring congruence classes, that we implement in the R package CRABS. Whereas existing methods constrain either the speciation rate or the extinction rate trajectory, ours provides more flexibility by sampling congruent speciation and extinction rate trajectories simultaneously. This allows covering a more representative set of distinct diversification rate trajectories. We also implement a filtering step that allows selecting the most parsimonious trajectories within a class.</li> <li>We demonstrate the utility of our new sampling strategy using a simulated scenario. Next, we apply our approach to the study of mammalian diversification history. We show that rapid changes in speciation and extinction rates need not be conserved across a congruence class, but that selecting the most parsimonious trajectories shrinks the class to concordant scenarios.</li> <li>Our approach opens new avenues both to truly explore the myriad of potential diversification histories consistent with a given phylogeny, embracing the uncertainty inherent to phylogenetic diversification models, and to select among these different histories. This should help refining our inference of diversification trajectories from extant data.</li> </ol>
Training dataset used in the magazine paper entitled "A Flexible Machine Learning-Aware Architecture for Future WLANs"
<p><a href="https://arxiv.org/pdf/1910.03510.pdf"><strong>A Flexible Machine Learning-Aware Architecture for Future WLANs</strong></a></p> <p><strong>Authors: </strong>Francesc Wilhelmi, Sergio Barrachina-Muñoz, Boris Bellalta, Cristina Cano, Anders Jonsson & Vishnu Ram.</p> <p><strong>Abstract: </strong>Lots of hopes have been placed in Machine Learning (ML) as a key enabler of future wireless networks. By taking advantage of the large volumes of data generated by networks, ML is expected to deal with the ever-increasing complexity of networking problems. Unfortunately, current networking systems are not yet prepared for supporting the ensuing requirements of ML-based applications, especially for enabling procedures related to data collection, processing, and output distribution. This article points out the architectural requirements that are needed to pervasively include ML as part of future wireless networks operation. To this aim, we propose to adopt the International Telecommunications Union (ITU) unified architecture for 5G and beyond. Specifically, we look into Wireless Local Area Networks (WLANs), which, due to their nature, can be found in multiple forms, ranging from cloud-based to edge-computing-like deployments. Based on ITU's architecture, we provide insights on the main requirements and the major challenges of introducing ML to the multiple modalities of WLANs.</p> <p><strong>Dataset description: </strong>This is the dataset generated for training a Neural Network (NN) in the Access Point (AP) (re)association problem in IEEE 802.11 Wireless Local Area Networks (WLANs). </p> <p>In particular, the NN is meant to output a prediction function of the throughput that a given station (STA) can obtain from a given Access Point (AP) after association. The features included in the dataset are:</p> <ol> <li>Identifier of the AP to which the STA has been associated.</li> <li>RSSI obtained from the AP to which the STA has been associated.</li> <li>Data rate in bits per second (bps) that the STA is allowed to use for the selected AP.</li> <li>Load in packets per second (pkt/s) that the STA generates.</li> <li>Percentage of data that the AP is able to serve before the user association is done.</li> <li>Amount of traffic load in pkt/s handled by the AP before the user association is done.</li> <li>Airtime in % that the AP enjoys before the user association is done.</li> <li>Throughput in pkt/s that the STA receives after the user association is done.</li> </ol> <p>The dataset has been generated through random simulations, based on the model provided in <a href="https://github.com/toniadame/WiFi_AP_Selection_Framework">https://github.com/toniadame/WiFi_AP_Selection_Framework</a>. More details regarding the dataset generation have been provided in <a href="https://github.com/fwilhelmi/machine_learning_aware_architecture_wlans">https://github.com/fwilhelmi/machine_learning_aware_architecture_wlans</a>.</p>
Affine Policies for Flexibility Provision by Natural Gas Networks to Power Systems
<p>Online appendix for the paper - "Affine Policies for Flexibility Provision by Natural Gas Networks to Power Systems", containing the power generators, gas producers and demand data as well as physical characteristics of the electrical transmission lines and gas pipelines. The empirically estimated forecast error covariance matrix used in the model is provided as well.</p> <p>The power systems and natural gas systems data is adapted from: C. Ordoudis, P. Pinson and J. M. González, "An integrated market for electricity and natural gas systems with stochastic power producers", European Journal of Operational Research, vol. 272, no. 2, pp. 642-654, 2019.</p>
Data from "Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques"
<p>DATA FILES from the study below:</p> <p><strong><a href="https://www.biorxiv.org/content/10.1101/603530v1">Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques</a></strong></p> <p>Jérôme Sallet, MaryAnn P Noonan, Adam Thomas, Jill X O’Reilly, Jesper Anderson, Georgios KPapageorgiou, Franz X Neubert, Bashir Ahmed, Jackson Smith, Andrew H Bell, Mark J Buckley, LéaRoumazeilles, Steven Cuell, Mark E Walton, Kristine Krug, Rogier B Mars, Matthew FS Rushworth</p> <p>bioRxiv 603530; doi: <a href="https://doi.org/10.1101/603530">https://doi.org/10.1101/603530</a></p> <p>*.nii.gz files could be opened with FSLeyes -<a href="https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes)">https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes)</a></p> <p>Dara are also available from : https://www.jeromesallet.org/data-ofc-reversal-learning</p>
Taxonomy of barriers that hinders Local Flexibility Market proliferation
<p>This dataset contains the results of the state of the art survey to retrieve the barriers that that hinders Local Flexibility Market proliferation. Scientific literature, interviews with different stakeholders, technical reports from the main energy agencies and the European and national legislation have been consulted to build the taxonomy. Three files are provided:</p> <ul> <li>a spreadsheet with the list of barriers and the document were it is found and</li> <li>a diagram with the end taxonomy of barriers</li> <li>a document with an explanation of the barriers included in each category of the taxonomy</li> </ul>
Prioritization of barriers that hinders Local Flexibility Market proliferation
<p>This dataset contains the prioritization provided by a panel of 15 experts to a set of 28 barriers categories for 8 different roles of the future energy system. A Delphi method was followed and the scores provided in the three rounds carried out are included. The dataset also contains the scripts used to assess the results and the output of this assessment. </p> <p>A list of the information contained in this file is:</p> <ul> <li> <p><strong>data folder</strong>: this folders includes the scores given by the 15 experts in the 3 rounds. Every round is in an individual folder. There is a file per expert that has the scores between -5 (not relevant at all) to 5 (completely relevant) per barrier (rows) and actor (columns). There is also a file with the description of the experts in terms of their position in the company, the type of company and the country.</p> </li> <li> <p><strong>fig folder</strong>: this folder includes the figures created to assess the information provided by the experts. For each round, the following figures are created (in each respective folder):</p> <ul> <li> <p>Boxplot with the distribution of scores per barriers and roles. </p> </li> <li> <p>Heatmap with the mean scores per barriers and roles.</p> </li> <li> <p>Boxplots with the comparison of the different distributions provided by the experts of each group (depending on the keywords) per barrier and role.</p> </li> <li> <p>Heatmap with the mean score per barrier weighted depeding on the importance of the role in each use case and the final prioritization.</p> </li> </ul> </li> </ul> <p>Finally, bar plots with the mean scores differences between rounds and boxplot with comparisons of the scores distributions are also provided.</p> <ul> <li> <p><strong>stat folder</strong>: this folder includes the files with the results of the different statistical assessment carried out. For each round, the following figures are created (in each respective folder):</p> <ul> <li> <p>The statistics used to assess the scores (Intraclass correlation coefficient, Inter-rater agreement, Inter-rater agreement p-value, Homogeneity of Variances, Average interquartile range, Standard Deviation of interquartile ranges, Friedman test p-value Average power post hoc) per barrier and per role.</p> </li> <li> <p>The results of the post hoc of the Friedman Test per berries and per roles.</p> </li> <li> <p>The average score per barrier and per role.</p> </li> <li> <p>The mean value of the scores provided by the experts grouped by the keywords per barrier and role. P-value of the comparison of these two values.</p> </li> <li> <p>The end prioritization of the barrier for the use case (averaging the scores or fuzzy merging of the critical sets)</p> </li> </ul> </li> </ul> <p>Finally, the differences between the mean and standard deviations of the scores between two consecutive rounds are provided.</p>
MD Data for Patterns in protein flexibility: a comparison of NMR "ensembles", MD trajectories and crystallographic B-factors
<p>This data set comprises five zipped directories that contain the scripts and intermediate molecular dynamics (MD) results used in (initially as of April 24, 2017, updated with additional directories on December 15, 2020) a soon to be submitted paper, "Patterns in protein flexibility: a comparison of NMR 'ensembles', MD trajectories and crystallographic B-factors" written by the authors of this entry. An earlier version of this paper is available via BioRxiv, DOI: https://doi.org/10.1101/240655.</p> <p>This paper explores patterns in coordinate variance and coordinate uncertainty in MD trajectories and in protein structures derived from NMR and compares coordinate variances/uncertainties with those crystallographic B-factors. The files, MD_data.zip and MD_data2.zip, each unzip to contain input files and scripts for reproducing the MD trajectories used in this paper (using DESMOND): MD_data.zip contains input files/scripts for the MD trajectories used in the preprint; MD_data2.zip contains input files/scripts for trajectories ran following publication of the preprint. The file btab_analysis_scripts.zip contains key scripts for analyzing those trajectories (following file conversion with VMD and superimposition with THESEUS) in MATLAB (this analysis assumes the presence of the FindCore Toolbox, written by David Snyder and available via the MATLAB Central File Exchange, as well as the MATLAB Statistics and Machine Learning Toolbox). And the files, superimposed_MD_trajectories.zip and superimposed_MD_trajectories2.zip, each unzip to yield the trajectories (superimposed using THESEUS and in PDB multimodel file format) analyzed in the soon to be submitted paper: superimposed_MD_trajectories.zip contains trajectories reported in the preprint and superimposed_MD_trajectories2.zip contains the results of subsequent simulations. </p>
Illustrative dataset for the article: Vieira, R., McDonald, S., Araujo-Soares, V., Sniehotta, F., Henderson, R. (2017) "Dynamic modelling of n-of-1 data: Powerful and flexible data analytics applied to individualised studies"
<p>This dataset is supplementary material of the manuscript "Dynamic modelling of n-of-1 data: Powerful and flexible data analytics applied to individualised studies. McDonald et al. (2016) presents a series of novel n-of-1 studies that intended to explore the relationship between physical activity change during the retirement transition. The file contains the data of one participant. The column names correspond to the following variables:</p> <p>time: duration of follow-up (minutes);<br> minute: time of day (hours and minutes);<br> day_num: day since beginning of follow-up (the first two days were considered as adaptation phase and therefore removed); <br> PAscore: accelerometer raw score; <br> startBout: 1 (a bout of PA was initiated in this minute) or 0 (a bout of PA wasn't <br> initiated in this minute); <br> nPAbouts_day: number of PA bouts per day; <br> nPAbouts_day.l1: number of PA bouts in previous day (lag 1); <br> nPAbouts_day.l2: number of PA bouts two day before (lag 2); <br> nBoutsLast2hours: number of PA bouts in previous 2 hours; <br> retirement: 0 (before retirement) or 1 (after retirement)<br> weekday: 0 (workday) or 1 (weekend)<br> sleepLength: number of hours of sleep last night<br> sleepLength.l1: number of hours of sleep the night before<br> sleepLength.l2: number of hours of sleep two nights before<br> pers: personalised measure of partner's influence (scale 0-1)<br> periodDay: morning, evening or afternoon</p> <p>McDonald, S., Vieira, R., O'Brien, N., White, M., & Sniehotta, F. F. (2016). Does physical activity and sedentary behavior change during the retirement transition? Findings from a series of novel n-of-1 natural experiments. <em>International Journal of Behavioral Medicine, 23</em>, S261-S261.</p> <p> </p>
Code and supplementary plots for "Flexible distributed lag models for count data using mgcv"
<p>R code and supplementary plots accompanying the paper: "Flexible distributed lag models for count data using mgcv".</p>
Flexible foliar stoichiometry with CTSM5.1
This data explores the importance of flexible foliar stoichiometry in mediating terrestrial carbon cycle and hydrologic responses to climate change using CTSM5.1. We find a strong reduction in terrestrial productivity and the land C sink over the 21st century when we allow foliar stoichiometry to increase with rising concentrations of CO2 in the atmosphere.
Integrating AlphaFold pLDDT Scores into CABS-flex for Enhanced Protein Flexibility Simulations
<div>This dataset accompanies the publication "Integrating AlphaFold pLDDT Scores into CABS-flex for enhanced protein flexibility simulations".</div> <div>This project was funded by the OPUS grant from the National Science Centre, Poland [2020/39/B/NZ2/01301].</div> <div> </div> <div>Training_set_protein_chains.txt and Whole_set_protein_chains.txt have lists of all PDB ID and chain used.</div> <div>Description_of_runs.csv has a list of every run tested. Run number correponds to csv file in Results_run.tar.gz.</div> <div>Every csv file has following columns: </div> <div> <ul> <li>PDB - PDB ID and chain </li> <li>Total_residues</li> <li>%_C - Percent of secondary structure assigned as coil by DSSP</li> <li>%_H - Percent of secondary structure assigned as helix by DSSP</li> <li>%_E - Percent of secondary structure assigned as sheet by DSSP</li> <li>%_T - Percent of secondary structure assigned as turn by DSSP</li> <li>pLDDT_mean - Average pLDDT score across all residues</li> <li>pLDDT_std - Standard deviation of pLDDT scores across all residues</li> <li>Unique_restraints - Number of unique restraints created by CABS-flex</li> <li>RMSF_CABS_R1_corr - RMSF correlation between CABS-flex and first MD simulation from ATLAS</li> <li>RMSF_CABS_R2_corr - RMSF correlation between CABS-flex and second MD simulation from ATLAS</li> <li>RMSF_CABS_R3_corr - RMSF correlation between CABS-flex and third MD simulation from ATLAS</li> <li>Highest_RMSF_corr - Highest RMSF correlation out of three</li> </ul> </div> <div> </div>
Flexible planar supercapacitors by straightforward filtration and laser processing steps
<p>Dataset for publication (journal article):</p><p>Flexible planar supercapacitors by straightforward filtration and laser processing steps</p><p>by Olli Pitkänen, Toprak Eraslan, Dániel Sebők, Imre Szenti, Ákos Kukovecz, Robert Vajtai and Krisztian Kordas</p><p>Published 22 September 2020 • © 2020 The Author(s). Published by IOP Publishing Ltd </p><p>Nanotechnology, Volume 31, Number 49 </p><p>Citation: Olli Pitkänen et al 2020 Nanotechnology 31 495403</p><p>DOI: 10.1088/1361-6528/abb336</p>
Intelligent Energy Systems Ontology: Local flexibility market and power system co-simulation demonstration
<p>The Intelligent Energy Systems Ontology (IESO) provides semantic interoperability within a society of multi-agent systems (MAS) developed in the scope of power and energy systems (PES). It leverages the knowledge from existing and publicly available semantic models developed for specific PES subdomains to accomplish a shared vocabulary among the agents of the MAS community, overcoming heterogeneity among the reused ontologies. IESO provides agents with semantic reasoning, constraints validation, and data uniformization. The use of IESO is demonstrated through the simulation of the management of a rural distribution network, considering the validation of the grid’s technical constraints. This dataset publishes files demonstrating: i) a snapshot of the initial semantic knowledge base (KB); ii) queries to the KB to get services inputs; iii) conversions between syntactic and semantic models; <br> iv) constraints validations; v) automatic conversion of units of measure.</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.