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Dataset results
37 results for “CoP”
CSI-COP Dataset of Organisations to Approach in Citizen Science Projects
<p>This dataset complements CSI-COP project deliverable D2.3 report: '<strong>Framework for Engaging Citizen Scientists</strong>'.</p> <p>The D2.3 deliverable was produced in 2020 by partners in CSI-COP work package 2 led by <strong>Professor Olga Stepankova </strong>of Czech Technical University, Prague (<strong>CTU</strong>).</p> <p>The Stepankova et al. (2020) report is available on this Zenodo platform here:</p> <p><a href="https://zenodo.org/record/4066515#.Yrx9DezMLb0">https://zenodo.org/record/4066515#.Yrx9DezMLb0</a></p>
CSI-COP Citizen Scientists App Investigations
<p>This open-access dataset presents project results from CSI-COP project work package 4 task T4.2 in which citizen scientists conducted investigations on apps in their smart devices. The App investigations dataset was created using Excel spreadsheet. Users can explore the information recorded by the citizen scientists by using the filters on the column headings, for example, on type of app (games; news, etc.), or language of app (including English, Greek, Hebrew).</p>
EU HORIZON2020 CSI-COP Anonymous dataset on Citizen scientists
<p>This dataset complements CSI-COP deliverable report D4.3: '<strong>CSI-COP Citizen Scientists Age, Gender, Socio-Economic and Geographical (AGSEG) Distribution Report</strong>'. </p><p>Researchers can review the dataset and analyse anonymous data on who the citizen scientists were in the EU Horizon2020 funded CSI-COP project with citizen scientists investigating GDPR compliance in websites and apps.</p>
Investigation of Azo-COP-2 as a photo-responsive low-energy CO2 adsorbent and porous filler in mixed matrix membranes for CO2/N2 separation
<p>Dataset supporting publication. All raw data for figures is included in the Excel file, and the full high-resolution SEM images are included in this repository.</p> <p>A preprint of the manuscript is available from <a href="https://doi.org/10.26434/chemrxiv.7593902">https://doi.org/10.26434/chemrxiv.7593902</a> </p> <p>The published paper is: Siyao Li, Nicholaus Prasetya, and Bradley P Ladewig, <em>Ind. Eng. Chem. Res.</em>, Just Accepted Manuscript<br> DOI: <a href="https://doi.org/10.1021/acs.iecr.9b00762">https://doi.org/10.1021/acs.iecr.9b00762</a></p>
PECD consistent heat demand and COP time series from when2heat
<p>This dataset uses the when2heat package to compute heat demand and COP time series. The data covers the years 1979 to 2019 and the sub-national level down to a resolution grouping Europe into 96 clusters based on ERA5 reanalysis data. Thanks to this temporal scope and spatial resolution, the data is consistent with the Pan-European Climatic Database (PECD) and enables multi-year analysis of multi-sector energy systems that include the heating sector.</p> <p>The "main data" folder provides the time series with COPs for various heat pump technologies and heat demand. The heat demand data deviates from the immediate when2heat outputs in two ways. First, it aggregates the demand for space heating and warm water in commercial and residential buildings into a single time series. The compositionHeatDemand.xlsx in the "background info" folder provides the weights assumed for this aggregation. Since the composition of heat demand changes in the future, the data includes a different time series for every fifth year from 2015 to 2050. Second, we normalize each profile over the historical years from 1979 to 2019 to its maximum value. Accordingly, multiplying the time series with maximum heat demand will give an absolute demand profile.</p> <p>A subfolder in "background_info" provides shape files describing the different spatial resolutions used. The "mappingRegions.csv" specifies which region names in this dataset correspond to region names in the PECD data. The "heating_thresholds.csv" in the "background info" folder provides the assumed heating thresholds to compute the heating demand.</p>
CSI-COP Citizen scientists website investigations
<p>This open-access dataset presents project results from CSI-COP project work package 4 task T4.1 in which citizen scientists conducted investigations on websites across the Internet. The website investigations' dataset was created using Excel spreadsheet. Users can explore the information recorded by the citizen scientists by using the filters on the column headings, for example, on type of website (games; news, etc.), or language of website (including English, Greek, Hebrew).</p>
CoP vs. CoM stabilization strategies: the tightrope balancing case
<p>This study proposes a generalization of the ankle and hip postural strategies to be applied to the large class of skills that share the same basic challenge of defeating the destabilizing effect of gravity on the basis of the same neuromotor control organization, adapted and specialized to variable number of degrees of freedom, different body parts, different muscles, and different sensory feedback channels. In all the cases we can identify two crucial elements (the CoP - Center of Pressure and the CoM - Center of Mass) and the central point of the paper is that most balancing skills can be framed in the CoP-CoM interplay and can be modeled as a combination/alternation of two basic stabilization strategies: the standard well investigated COPS (or CoP strategy, the default option), where the CoM is the controlled variable and the CoP is the control variable, and the less investigated COMS (or CoM strategy), where CoP and CoM must exchange their role because the range of motion of the CoP is strongly constrained by environmental conditions.</p> <p>The paper focuses on the tightrope balancing skill where sway control in the sagittal plane is modeled in terms of the COPS while the more challenging sway in the coronal plane is modeled in terms of the COMS, with the support of a suitable balance pole. Both stabilization strategies are implemented as state-space intermittent, delayed feedback controllers, independent of each other. Extensive simulations support the degree of plausibility, generality, and robustness of the proposed approach.</p>
Application of CoLD-CoP to Detecting Competitively and Cooperatively Binding Ligands
<p>CompetitiveCooperative_CoLD_CoP_for_archiving.zip will unzip into a directory with two sub-directories. The directory LysozymeNAG has directories with raw data (and NMRPipe format spectra processed in the direct dimension) for DOSYs acquired with a ligands only sample, a ligands + lysozyme sample and a ligands + lysozyme + NAG (N-Acetyl-Glucosamine) sample. This directory also has the key scripts used for DOSY processing and analysis of this dataset. The directory TyrosinaseHCCA similarly has directories with raw data (and NMRPipe format spectra processed in the direct dimension) for DOSYs acquired with a ligands only sample, a ligands + (mushroom) tyrosinase sample and a ligands + tyrosinase + HCCA (4-Hydroxy-a-Cyano-Cinnamic Acid) sample. The TyrosinaseHCCA directory has not only the key scripts used for DOSY processing and analysis of this dataset, but it also has 1D proton spectra acquired on each solution used in this arm of the project. </p> <p>The file das_dosy_nm.m is a customized version of the DOSY processing file used in DOSY processing instead of the dosy_nm.m file used in the DOSY Toolbox. Use of the scripts found in the directories described in the above paragraph requires das_dosy_nm.m as well as NMRPipe, the DOSY Toolbox (https://nmr.chemistry.manchester.ac.uk/?q=node/8), CoLD-CoP toolbox and Covariance Toolbox (both available via MATLAB Central File Exchange).</p> <p>The file lysozyme_complexes_for_docking.zip contains lysozyme structures as minimized in various complexes as well as the ligands from those complexes. These can be used for rescoring or further re-docking, e.g., in FastDRH. DockingAndEnergyMinimization.zip contains some of the files used for docking and energy minimization of the Lysozyme-NAG, Lysozyme-Tris and Lysozyme-GlcNAC complexes. </p>
Benchmark cancer datasets for Clustering algorithms for Omics-based Patient Stratification (COPS)
<p>This repository contains seven multi-omic cancer datasets including several cancer types (breast, kidney, lung, ovary, prostate, and thyroid cancers as well as low grade gliomas) that were used for benchmarking several multi-view clustering algorithms implemented by COPS (https://github.com/UEFBiomedicalInformaticsLab/COPS). The datasets were originally compiled from The Cancer Genoma Atlas (TCGA) and downloaded using the <em>curatedTCGAData</em> R-package. The datasets include copy-number variations, methylomics as well as mRNA and miRNA transcriptomics. The methylomics data was mapped to genes by averaging methylation level of probes associated with the promoter regions of genes. Similarly the miRNA transcriptomics data was mapped to genes by using known and predicted miRNA -> gene interactions. Updated survival data was acquired from the Liu et al. 2018 paper. </p> <p>This repository also includes two sets of cancer associated pathway networks used by pathway-based multi-omic methods benchmarked in our study. NCI-PID pathways were downloaded using the <em>ndexr</em> R-package on December 22 2021. While KEGG pathways were downloaded using the <em>pathview</em> R-package on May 3 2022. </p> <p>More details on the processing can be found on the related publication.</p>
ClimEmpower: Graphic summaries of CoPs' key points
<p>Comprehensive visual representations of key findings from initial CoP meetings across ClimEmpower's regions. It includes the main insights and conclusions structured around eight categories: "hazard identification", "ttols and services", "expectations", "needs and solutions", "gaps and barriers", "previous experiences", "contributins" and "other comments".</p>
Comparative Study of Fluticasone Furoate(FF)/Umeclidinium Bromide (UMEC)/ Vilanterol (VI) Closed Therapy Versus FF/VI Plus UMEC Open Therapy in Subjects With Chronic Obstructive Pulmonary Disease (COP
ClinicalTrials.gov study NCT02729051. IPD Sharing: YES. Countries: 12. Publications: 2.
A Study to Evaluate the Efficacy and Safety of Umeclidinium Bromide/Vilanterol Compared With Fluticasone Propionate/Salmeterol Over 12 Weeks in Subjects With Chronic Obstructive Pulmonary Disease (COP
ClinicalTrials.gov study NCT01822899. IPD Sharing: YES. Countries: 8. Publications: 1.
CoP vs. CoM stabilization strategies: the tightrope balancing case
Open the record for dataset details and reuse information.
Colchicine For The Prevention Of Perioperative Atrial Fibrillation In Patients Undergoing Thoracic Surgery (COP-AF)
ClinicalTrials.gov study NCT03310125. IPD Sharing: UNDECIDED. Countries: 11. Publications: 3.
Comparison of Lower Limb COP and Muscle Activation During Single-Leg Deadlift Using Elastic and Inelastic Barbells
ClinicalTrials.gov study NCT07061314. IPD Sharing: YES. Countries: 1. Publications: 32.
Caries-Obesity Prevention in Children by Modifying Risk Behaviors Among Parents With Immigrant Background-COP Study
ClinicalTrials.gov study NCT05758454. IPD Sharing: NO. Countries: 1. Publications: 3.
Host transcriptional reprogramming in response to the rice bacterial blight pathogen Xanthomonas oryzae pv.oryzae constitutively expressing the X. oryzae pv. oryzicola TAL2a effector from the high cop
GEO Series GSE67958. Oryza sativa Japonica Group. 6 samples. Type: Expression profiling by high throughput sequencing.
Alfa2-COP is involved in early secretory traffic in Arabidopsis and it is required for plant growth
GEO Series GSE81049. Arabidopsis thaliana. 4 samples. Type: Expression profiling by array.
Revealing nascent RNA processing dynamics with nano-COP
GEO Series GSE154079. synthetic construct; Homo sapiens. 11 samples. Type: Other.
Investigating the Neuroprotective Effect of Cop-1 (Copaxone) in Acute Primary Angle Closure
ClinicalTrials.gov study NCT01936129. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.