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6,782 results for “project”
BIOHARV project INTERREG V - Piezoelectric biobased polymers - Data set n°2
<p>The BIOHARV projet is financed by FEDER, Wallonia Region, West-Vlaanderen Region and Agentshap Innoveren & Ondernemen. BIOHARV project started on 2016, October 1st for 4 years. Six academic & technological partners are gathered (Institute Mines Telecom North Europe, Armines, University of Mons, Centexbel, University of Lille and Polytechnic University Hauts-De-France. The main objective of the BIOHARV project is demonstrate & investigate piezoelectric, electroactive & electromechanical properties of biobased polymers. </p> <p><strong>This dataset includes the physico-cheminal properties for various types of PLA processed by extrusion-MDO (machine-direction orientation) in relation to their shear piezoelectric properties. Various characterization techniques are used such as DSC, (2D-)WAXS, (2D-)SAXS and FTIR (non polarized and polarized). The related article "Shear Piezoelectricity of Poly(L-Lactide) Films Manufactured by Extrusion – Orientation: An Insight on Process – Structure – Properties Relationships" will be published soon.</strong></p>
Available plasmids used in year 1 of ASAP project 2 - Target Enablement.
<p>Collated plasmids used for the ASAP project 2 drug discovery platform in year 1. Details of insert sequence and vector backbone included. These plasmids will be deposited into Addgene shortly.</p> <p>Plasmids will be accessible for use by all. </p> <p>Plasmids used in successful crystal screens to date are highlighted on the sheet.</p> <p>Up to date information on the progress made by the ASAP project 2 team can be found here: https://asapdiscovery.org/outputs/target-enabling-packages/</p> <p> </p> <p> </p> <p>Research reported here was supported in part by NIAID of the National Institutes of Health under award number U19AI171399.The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health</p>
maDeepLabCut 3-mouse demo project
<p>see https://github.com/DeepLabCut/DeepLabCut/blob/main/examples/COLAB/COLAB_3miceDemo.ipynb </p>
Data of crop yield derived from H2020 Diverfarming project
<p>Crop yield data of a mandarin crop (main crop) and different secondary crops (alley crops) cultivated in different cropping systems (monocrop and diversification) with different treatments (control irrigation and regulated deficit irrigation)</p>
Data from the Bioradar Work Package of the German BMBF Research Project SORTIE
<p>This research is part of the German research project SORTIE and is supported by the German Federal Ministry of Education and Research (BMBF) under grant number 13N15189.</p> <p>Related journal articles:</p> <p><em>[1]</em> <em>Shi, D.; Gidion, G.; Aftab, T.; Reindl, L.M.; Rupitsch, S.J. Frequency Comb-Based Ground-Penetrating Bioradar: System Implementation and Signal Processing. Sensors 2023, 23, 1335. </em><a href="https://doi.org/10.3390/s23031335">https://doi.org/10.3390/s23031335</a><em>.</em></p> <p><em>[2] Shi, D.; Gidion, G.; Reindl, L.M.; Rupitsch, S.J. Automatic life detection based on efficient features of ground-penetrating rescue radar signals. Submitted to Sensors.</em></p>
Code and Data to support "Atmospheric circulation-constrained model sensitivity recalibrates Arctic climate projections"
<p><a href="https://zenodo.org/api/files/b2d03cf8-c9e1-4ffb-8120-eb08237612e6/sic.sep.5member.dat">sic.sep.5member.dat</a> contains direct binary data of spatial monthly averaged sea ice concentrations for 1979 January to 2020 December from the CESM2 wind-nudging runs.</p> <p><a href="https://zenodo.org/api/files/b2d03cf8-c9e1-4ffb-8120-eb08237612e6/cism2.exp.smb.01.nc">cism2.exp.smb.01.nc</a> to <a href="https://zenodo.org/api/files/b2d03cf8-c9e1-4ffb-8120-eb08237612e6/cism2.exp.smb.01.nc">cism2.exp.smb.05.nc</a> contain netcdf files of annual averaged surface mass balance output from the CESM2-CISM2 wind-nudging runs between 1979 and 2020.</p> <p>topal&ding_code1.py - data preparation Python code</p> <p>topal&ding_code2.py - creating the main text and supplementary figures.</p>
Spectra and retrieval data from CO and HCN observations of Jupiter with ALMA (project 2016.1.01235.S)
<p>Spectra and retrieval data from:</p> <p>Title: Evidence for auroral influence on Jupiter's nitrogen and oxygen chemistry revealed by ALMA<br> Authors: T. Cavalié, L. Rezac, R. Moreno, E. Lellouch, T. Fouchet, B. Benmahi, T. K. Greathouse,<br> J. A. Sinclair, V. Hue, P. Hartogh, M. Dobrijevic, N. Carrasco, Z. Perrin<br> Journal: Nature Astronomy<br> Year: 2023</p> <p>########################<br> # Description of files #<br> ########################</p> <p>CO_data/<br> latitudes_CO.dat: planetocentric latitudes of the 540 pointings from which the CO spectra have been extracted<br> spectrum_*.dat : CO spectra, one per pointing. Frequency in GHz and continuum-subtracted flux density in Jy/beam<br> profiles_*.dat : vertical profiles, one per pointing. Pressure in mbar, temperature in K (local average, see paper for details), and retrieved CO volume mixing ratio</p> <p>HCN_data/<br> latitudes_HCN.dat: planetocentric latitudes of the 557 pointings from which the HCN spectra have been extracted<br> spectrum_*.dat : HCN spectra, one per pointing. Frequency in GHz and continuum-subtracted flux density in Jy/beam<br> profiles_*.dat : vertical profiles, one per pointing. Pressure in mbar, temperature in K (local average, see paper for details), and retrieved HCN volume mixing ratio</p> <p> </p>
USEPE Project Scenarios Dataset
<p><strong>Scenarios</strong></p> <p>Scenario files, with configuration and data, for USEPE simulations, both reference and solution (D2C2) scenarios.</p> <p><strong>Exercises included</strong></p> <p>1. Last Mile Delivery (In directory exercise_1)<br> 2. Emergency Services (In directory exercise_2)<br> 3. Urban Surveillance (In directory exercise_3)</p> <p>Exercise structure:</p> <ul> <li>settings file (.cfg) - This file defines the input data to be loaded in a specific experiment. </li> <li>data folder - the input data required for the experiments. Some data is not included due to large size or property restrictions.</li> <li>output - raw outputs obtained from the experiments in the USEPE project</li> <li>scenario - BlueSky scenario files (.scn) required for the experiment </li> </ul> <p><strong>How to</strong><br> See how_to_setup_for_simulations.pptx for step-by-step explanation on how to use these files to create runnable scenarios for BlueSky with the USEPE plugin.<br> </p>
Development of Robotics & Automation Roadmap for Road Construction/Maintenance Projects
<p>Over the past few decades, there have been no significant improvements in the construction industry's productivity. Outside the construction industry, robots have proven to be beneficial in improving productivity, safety, and quality. There is a growing interest in adopting robots in the construction industry to address these issues. As robots get introduced to the project sites, human-robot interaction becomes an important consideration for wide-scale adoption. The success of deploying robots in the construction industry depends on how well humans collaborate with the robots. This project focused on investigating the perception of various construction industry stakeholders in adopting robots in their projects, and the level of control they feel can be delegated to robots. This project also investigated major factors that impact the decision to use a robot for a particular task. The research used a survey to collect relevant data from various construction industry stakeholders for 11 specific types of robots that were applicable to the construction industry. The main findings from this study are that there was a generally positive perception of different types of robots regarding their job relevance, ease of use, output quality, and usefulness. Furthermore, from a human-robot collaboration perspective, majority of respondents supported shared autonomy between human and robot in different scenarios. This highlights the need for developing more robots that share control with the human when executing construction tasks.</p>
Telethon EEG project
<p>In this study, we focus on brain activation as a proxy measure of video effectiveness and we compare within a multivariate setting physiological responses induced by four fundraising campaigns realized using different communication strategies. We model electroencephalographic (EEG) signals using methodological tools belonging to graphical models to estimate partial correlation networks corresponding to different advertisements. The structure of estimated EEG networks is then compared in posthoc analysis using resampling procedures.</p>
Project files provided as supporting information to the manuscript "Kinetics of radiation-induced DNA double-strand breaks through coarse-grained simulations"
<p><strong>README file to the project files provided as supporting information to the manuscript “Kinetics of radiation-induced DNA double-strand breaks through coarse-grained simulations"</strong></p> <p>Authors: Manuel Micheloni, Lorenzo Petrolli, Gianluca Lattanzi and Raffaello Potestio<br> ==================================</p> <p>The .zip file is structured as follows:</p> <p>##############<br> 0_DNA_Sequence<br> ##############</p> <p>The folder contains:<br> • DNAsequence.txt: the DNA molecule employed in the study. <br> • ecseq.txt: the structure of the DNA. Each nucleotide (LAMMPS residue ID) is associated with the chemical type of the nucleic base.</p> <p>###################<br> 1_DSB_MDSimulations<br> ###################</p> <p>• MDSimulations: <br> The LAMMPS MD simulation scripts.<br> The subfolders are structured and named after different DSB motifs (folders 0,1, …,3) providing different external forces (folders 1000, 1100, 1200 refers to the respective average end-to-end distance). The latter subfolders provides:</p> <p>I. LMP_script provides the LAMMPS input file.</p> <p>II. the local internal energy contributions from the nucleotides involved with the residual contact interface between broken DNA moieties (data/1.E); </p> <p>III. coordinates of the nucleotides involved with the residual contact interface between the DNA moieties (data/2.POS);</p> <p>IV. the binary LAMMPS data file, employed as starting coordinates for the DSB MD simulation (/input_structure).</p> <p><br> • ScriptsAndAnalysis:<br> The scripts employed to analyze the DSB process. </p> <p>I. /00_GeneralScripts all the MATLAB functions employed for the analysis.</p> <p>II. /1_SigmoidalFitting provides the scripts from a sigmoidal fitting procedure [1] on the internal energy profile of the nucleotides between the strand breaks (Section 2 of the Supplementary Material). <br> [1] R P (2022). sigm_fit (https://www.mathworks.com/matlabcentral/fileexchange/42641-sigm_fit), MATLAB Central File Exchange. Retrieved July 2, 2022.</p> <p><br> ##################<br> 2_DNAFreeDiffusion<br> ##################</p> <p>• SIM:<br> It contains the MD simulations of the freely-diffusing DNA. Namely, the MSD of the DNA molecule, the input structure, and the LAMMPS simulation scripts are reported in /data, /input_structure, and /LMP_scripts respectively.</p> <p><br> • ANALYSIS:<br> It provides the script employed to estimate the diffusion coefficient of the DNA molecule and the time-scaling factor \Gamma(\zeta).</p> <p>#######################<br> 3_ForceAnalysis<br> #######################</p> <p>• data<br> It contains the forces computed from the MD simulations of the intact DNA molecules subject to the external force of 0.42, 0.88 and 3.06 pN. All data are stored in pickle format.<br> The README file contains additional information.</p> <p><br> NOTE1: Most data/scripts are saved according to the format .mat, employed by MATLAB Ⓡ, a numerical computing environment and proprietary programming language developed by MathWorks.</p> <p>NOTE2: For the force data manipulation, we acknowledge the use of LammpsFileManipulation package (https://pypi.org/project/LammpsFileManipulation/)</p>
The proteomics raw data from project MIPHA
<p>The proteomics data and search result from the hisD overexpression strains and control strains treated with or without levofloxacin. </p>
[Tool demo] Prospector: a Tool to Find Fixes to Known Vulnerabilities of Open-Source Projects
<p> </p> <p><strong>>>>> TOOL DEMO SCREENCAST</strong>: please download file <strong><a href="https://zenodo.org/record/7974442/files/tool_demo_final.mp4?download=1">tool_demo_final.mp4</a></strong>. <strong><<<<</strong></p> <p> </p> <p><strong>ABSTRACT</strong>: Though vulnerability databases are key for monitoring known vulnerabilities in open-source projects, they rarely contain information about the code changes that fix the flaws they describe. Finding them is time-consuming and error-prone as it involves the analysis of multiple, unstructured resources. </p> <p>In this paper we present \prospector, a tool that supports mapping<br> vulnerability advisories from vulnerability databases onto the corresponding fix in the source code. \prospector employs a set of heuristics that mimics and automates the<br> strategies that would be employed by human security experts.</p> <p>Given an advisory expressed in natural language, \prospector processes the commits found in the target source code repository, ranks them based on a set of predefined rules, and produces a report that the user can inspect to<br> determine which commits to retain as the actual fix.<br> The tool is publicly available and is released under the Apache 2.0 license.</p>
Projecting Residential Energy Consumption across Multiple Income Groups under Decarbonization Scenarios using GCAM-USA
<p>Understanding the residential energy consumption patterns across multiple income groups under decarbonization scenarios is crucial for designing equitable and effective energy policies that address climate change while minimizing disparities. This dataset is developed using an integrated human-Earth system model, supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment at Pacific Northwest National Laboratory (PNNL).</p> <p>GCAM-USA operates within the Global Change Analysis Model, which represents the behavior of, and interactions between, different sectors or systems, including the energy system, the economy, agriculture and land use, water, and the climate. GCAM is one of only a few integrated global human-Earth system models, also known as Integrated Assessment Models (IAMs), which address key processes in inter-linked human and earth systems and provide insights into future global environmental change under alternative scenarios (IAMC, 2022).</p> <p>GCAM has global coverage with varying spatial disaggregation depending on the type of system being modeled. For energy and economy systems, 32 regions across the globe, including the USA as its own region, are modeled in GCAM. GCAM-USA advances with greater spatial detail in the USA region, which includes 50 States plus the District of Columbia (hereinafter “state”). The core operating principle for GCAM and GCAM-USA is market equilibrium. The model solves every market simultaneously at each time step where supply equals demand and prices are endogenous in the model. The official documentation of GCAM and GCAM-USA can be found at: <a href="https://jgcri.github.io/gcam-doc/toc.html">https://jgcri.github.io/gcam-doc/toc.html</a></p> <p>The dataset included in this repository is based on an improved version of GCAM-USA v6, where multiple consumer groups, differentiated by the average income level for 10 population deciles, are represented in the residential building energy sector. As of May 15, 2023, the latest officially released version of GCAM-USA has a single consumer (represented by average GDP <em>per capita</em>) in the residential sector and thus does not include this feature. This multiple-consumer feature is important because (1) demand for residential floorspace and energy are non-linear in income, so modeling more income groups improves the representation of total demand and (2) this feature allows us to explore the distributional effects of policies on these different income groups and the resulting disparity across the groups in terms of residential energy security. If you need more information, please contact the corresponding author.</p> <p>Here, we ran GCAM-USA with the multiple-consumer feature described above under four scenarios over 2015-2045 (Table 1), including two business-as-usual scenarios and two decarbonization scenarios (with and without the impacts of climate change on heating and cooling demand). This repository contains the key output variables related to the residential building energy sector under the four scenarios, including:</p> <ul> <li>income shares by consumer groups at each state over 2015-2045 (Casper et al. 2022)</li> <li>residential energy consumption <em>per capita</em> by service and fuel, by state and income group, 2015-2045</li> <li>residential energy service output (energy consumption * technology efficiency) <em>per capita</em> by service, fuel, and technology, by state and income group, 2015-2045</li> <li>estimated energy burden (Eq.1), by state and income group, 2015-2045</li> <li>residential heating service inequality (Eq.2), by state, 2015-2045</li> </ul> <p> </p> <p><strong>Table 1</strong></p> <table> <thead> <tr> <th scope="col">Scenarios</th> <th scope="col">Policies</th> <th scope="col">Climate Change Impacts</th> </tr> </thead> <tbody> <tr> <td>BAU (Business-as-usual)</td> <td>Existing state-level energy and emission policies</td> <td>Constant HDD/CDD (heating degree days / cooling degree days)</td> </tr> <tr> <td>BAU_climate</td> <td>Existing state-level energy and emission policies</td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> <tr> <td>NZnoCCS (Net-Zero by 2050 without CCS)</td> <td> <p>Two national targets:</p> <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Constant HDD/CDD</td> </tr> <tr> <td>NZnoCCS_climate</td> <td> <p>Two national targets:</p> <ul> <li>50% net-GHG emission reduction relative to 2005 level and net-zero GHG emissions by 2050</li> <li>US power grid achieves clean-grid by 2035</li> </ul> </td> <td>Projected state-level HDD/CDD through 2100 under RCP8.5</td> </tr> </tbody> </table> <p> </p> <p><strong>Eq. 1</strong></p> <p><span class="math-tex">\(Energy\ burden_i = \dfrac{\sum_j (service\ output_{i,j} * service\ cost_j)}{GDP_i}\)</span></p> <p>for income group<em> i</em> and service <em>j</em></p> <p> </p> <p><strong>Eq. 2</strong></p> <p><strong><span class="math-tex">\(Residential\ heating\ service\ inequality = \dfrac{S_{d10}}{(S_{d1} +S_{d2} + S_{d3} + S_{d4})}\)</span></strong></p> <p>where <em>S</em> is the residential heating service output <em>per capita</em> of the highest income group (<em>d10</em>) divided by the sum of that of the lowest four income groups (<em>d1</em>, <em>d2</em>, <em>d3</em>, and <em>d4</em>), similar to the Palma ratio often used for measuring income inequality. A higher Palma ratio indicates a greater degree of inequality.</p> <p> </p> <p><strong>Reference</strong></p> <p>Casper, Kelly, Narayan, Kanishka B., O'Neill, Brian C., & Waldhoff, Stephanie. 2022. State level income distributions for net income deciles for the US for historical years (2011-2014) and projections for different SSP scenarios (2015-2100) (latest version obtained from the authors on April 6, 2023) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7227128">https://doi.org/10.5281/zenodo.7227128</a></p> <p>IAMC. 2022. The common Integrated Assessment Model (IAM) documentation [Online]. Integrated Assessment Consortium. Available: https://www.iamcdocumentation.eu/index.php/IAMC_wiki [Accessed May 2023].</p> <p> </p> <p>This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).</p> <p>PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.</p> <p> </p>
Results of the Macro-Economic Analysis by the REMES model in the openENTRANCE project
<p>This dataset contains scenario results from the REMES:EU model as part of the macro-economic analysis in the openENTRANCE project.</p> <p>The data file follows the IAMC data format and the conventions established by the openENTRANCE project. See <a href="https://github.com/openENTRANCE/openentrance">https://github.com/openENTRANCE/openentrance</a> for details.</p> <p>Visit the openENTRANCE Scenario Explorer at <a href="https://data.ene.iiasa.ac.at/openentrance">https://data.ece.iiasa.ac.at/openentrance</a> for more information about the openENTRANCE project and other datasets.</p>
Results for Case Study 5 of the openENTRANCE project
<p>This dataset contains scenarios results for Case Study 5 of the openENTRANCE project.</p> <p>Visit the openENTRANCE Scenario Explorer at <a href="https://data.ene.iiasa.ac.at/openentrance">https://data.ece.iiasa.ac.at/openentrance</a> for more information.</p>
Results for Case Study 1 of the openENTRANCE project
<p>This dataset contains scenarios results for Case Study 1 of the openENTRANCE project.</p> <p>Visit the openENTRANCE Scenario Explorer at <a href="https://data.ene.iiasa.ac.at/openentrance">https://data.ece.iiasa.ac.at/openentrance</a> for more information.</p>
Table 2. Studies on psychiatric side effects of fluoroquinolones. The risk of bias and study quality assessed with the Effective Public Health Practice Project's Quality Assessment Tool for Quantitative Studies (QATQS) was presented as the global rating for each publication (3 – weak).
<p>Studies on psychiatric side effects of fluoroquinolones. The risk of bias and study quality assessed with the Effective Public Health Practice Project’s Quality Assessment Tool for Quantitative Studies (QATQS) was presented as the global rating for each publication (3 – weak).</p>
Guided a Research Project titled "Bottle Biospheres: An Innovative Idea to understanding life" which was submitted by Ms. Dakhni Fatima in Pure Sciences. December 12, 2022.
<p>Guided a Research Project titled <strong>“Bottle Biospheres: An Innovative Idea to understanding life” </strong>which was submitted by <strong>Ms. Dakhni Fatima </strong>in <strong>Pure Sciences in Aavishkar. December 12, 2022</strong></p>
200 Python selected projects for various software engineering studies
<p>History of 200 open-source Python projects hosted on GitHub - git clone at Feb 2022</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.