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3,443 results for “technology”
CCG Starter Kits - Technology-specific data for Base SAND file
<p>These files contain the Capacity Factors and Residual Capacity values for all countries needed for filling in the information in the base SAND file.</p> <p>This is published as part of the MethodsX paper titled <strong>How to put together a Starter Data Kit from scratch? An extensive methodology to compile zero-order energy transition models. </strong>The main goal of the files published for this paper is to develop a set of credible data and an initial investment model for several developing countries.</p>
Energy Efficiency Technologies Costs - Support File for Data Manipulation Starter Data Kits
<p>This file can be used to manipulate the Energy Efficiency Technologies Costs data for the Starter Data Kits. </p>
Storage Technologies Costs - Support File for Data Manipulation Starter Data Kits
<p>This file can be used to manipulate the storage technologies cost data for the Starter Data Kits. </p>
New HMI technologies by CARBODIN
<p>This video presents the activities performed within CARBODIN WS10 investigating innovative technologies for advanced Human Machine Interface in railway transport.</p>
Supplementary files - Evaluation of MALDI-TOF MS technology in small ruminant milk adulteration using raw bovine milk
<p>The dataset is a part of Supplementary file for the manuscript:</p> <p><strong>Evaluation of MALDI-TOF MS technology in small ruminant milk adulteration using raw bovine milk</strong> by L. Rysova, P. Cejnar, O. Hanus, V. Legarova, J. Havlik, H. Nejeschlebova, I. Nemeckova, R. Jedelska, M. Bozik, submitted to <em>Journal of Dairy Science</em> (Manuscript ID JDS.2021-21396), Received October 8, 2021, Accepted January 31, 2022, Corresponding author: bozik@af.czu.cz, <a href="https://doi.org/10.3168/jds.2021-21396">https://doi.org/10.3168/jds.2021-21396</a></p> <p><strong>File 1:</strong> Detailed MALDI-TOF method description</p> <p><strong>File 2: </strong>Quantification of milk adulteration – calibration of the model Quantification of milk adulteration – calibration of the model</p> <p><strong>Table S1: </strong>Baseline characteristics of pure bovine milk which was used as an adulterant of caprine milk<strong> </strong></p> <p><strong>Table S2: </strong>Baseline characteristics of pure bovine milk which was used as an adulterant of ovine milk</p> <p><strong>Table S3: </strong>Root mean squared error (RMSE) of predicted caprine and ovine adulterated milk samples using set A as the training set and set B as the test set.</p> <p><strong>Table S4: </strong>Root mean squared error (RMSE) of predicted caprine and ovine adulterated milk samples using both, set A and set B , as the one training set and set C as the test set.</p> <p><strong>Table S5: </strong>Root mean squared error (RMSE) of predicted caprine and ovine adulterated milk samples using set AB as the training set and set C as the test set.</p> <p>In this version <strong>SD values in Table S2 were corrected</strong>.</p>
TIMES-Sweden Fuel production technologies database
<p>This is a database containing techno-economic data for fuel production technologies, including data for stand-alone technologies, technologies co-producing district heating and technologies producing heat for integration with industries. The database is a compilation of information from literature, specifically tailored for use in TIMES models. Even though this specific database has been developed for TIMES-Sweden, the data can also be applied for other regions. The database is continuously updated as work progresses with the TIMES-Sweden model.</p> <p>Preferably to be used in combination with TIMES-Sweden Industry database (<a href="https://doi.org/10.5281/zenodo.4139800">10.5281/zenodo.4139800</a>), and TIMES-Sweden (Industrial) Heat generation technologies database (<a href="https://doi.org/10.5281/zenodo.6372930">10.5281/zenodo.6372930</a>).</p> <p>This Database is also a part of the IEA ETSAP SubRES project, with the aim to make techno-economic data more accessible. More information about ETSAP can be found here: <a href="https://iea-etsap.org/">https://iea-etsap.org/</a></p> <p>More information about TIMES-Sweden and the modelling team can be found here: <a href="http://www.ltu.se/TIMES-Sweden">http://www.ltu.se/TIMES-Sweden</a></p>
TIMES-Sweden (Industrial) Heat generation technologies database
<p>This is a database containing techno-economic data for heat & power technologies, primarily focusing on technologies for heat generation in industry or district heating. The database is a compilation of information from literature, specifically tailored for use in TIMES models. Even though this specific database has been developed for TIMES-Sweden, the data can also be applied for other regions. The database is continuously updated as work progresses with the TIMES-Sweden model.</p> <p>Preferably to be used in combination with TIMES-Sweden Industry database (<a href="https://doi.org/10.5281/zenodo.4139800">10.5281/zenodo.4139800</a>), and TIMES-Sweden Fuel production technologies database (<a href="https://doi.org/10.5281/zenodo.6372926">10.5281/zenodo.6372926</a>).</p> <p>This Database is also a part of the IEA ETSAP SubRES project, with the aim to make techno-economic data more accessible. More information about ETSAP can be found here: <a href="https://iea-etsap.org/">https://iea-etsap.org/</a></p> <p>More information about TIMES-Sweden and the modelling team can be found here: <a href="http://www.ltu.se/TIMES-Sweden">http://www.ltu.se/TIMES-Sweden</a></p>
Home-based measurements of dystonia and choreoathetosis in cerebral palsy using smartphone-coupled inertial sensor technology and machine learning: A proof-of-concept study - dataset
<p>Home-based measurements of dystonia in cerebral palsy using smartphone-coupled inertial sensor technology and machine learning: A proof-of-concept study</p> <p> </p> <p>This project contains:</p> <p>- 1 main MATLAB script: MODYSathome_main.m<br> - 12 MATLAB functions:<br> - function_calc_mean_recall_precision.m<br> - function_create_dataframes.m<br> - function_deep_learning.m<br> - function_determine_best_ML_model.m<br> - function_display_DL_results.m<br> - function_display_ML_results.m<br> - function_index_extremities.m<br> - function_machine_learning.m<br> - function_oversample.m<br> - function_partition_data.m<br> - function_pick_best_models.m<br> - function_prepare_DL_data.m</p> <p>Downloading the Matlab scripts</p> <p> - Create a folder named 'MODYS' and create a subfolder named 'results'<br> - Download the zip file via <a href="https://zenodo.org/record/6379348">RehabAUmc/modys-at-home: v1.0 | Zenodo</a><br> - Unzip the zip file in the path MODYS\</p> <p>STEPS<br> 1. Open MATLAB<br> 2. In MATLAB, go to the 'HOME' tab and click on 'Set Path'<br> 3. Click on 'Add Folder' and browse to MODYS/RehabAUmc-modys-at-home-86b14c3/functions<br> 4. Click on 'Select Folder' and click on 'Save'<br> 5. Click on 'Browse to folder' and browse to a patients' data in MODYS/data/PatientXXX, then click on 'Select Folder'<br> 6. In the 'HOME' tab click on 'Open' and open MODYSathome.m in MODYS/RehabAUmc-modys-at-home-86b14c3<br> 7. In the 'EDITOR' tab click on 'Run Section' to run the script<br> 8. When the code has been run, the results are displayed in the Command Window and saved in MODYS/results/PatientXXX</p>
Data of paper Chemically vapor deposited Eu3+:Y2O3 thin films as a material platform for quantum technologies
<p>Data of the figures of the paper:</p> <p>N. Harada, A. Ferrier, D. Serrano, M. Persechino, E. Briand, R. Bachelet, I. Vickridge, J.-J. Ganem, P. Goldner, and A. Tallaire, <em>Chemically Vapor Deposited Eu 3+:Y 2O 3thin Films as a Material Platform for Quantum Technologies</em>, J. Appl. Phys. <strong>128</strong>, 055304 (2020). doi: <a href="https://doi.org/10.1063/5.0010833">10.1063/5.0010833</a></p> <p> </p>
Data of paper Controlling the interfacial reactions and environment of rare-earth ions in thin oxide films towards wafer-scalable quantum technologies
<p>Data of the figures in the paper :</p> <p>N. Harada, A. Tallaire, D. Serrano, A. Seyeux, P. Marcus, X. Portier, C. Labbé, P. Goldner, and A. Ferrier, <em>Controlling the Interfacial Reactions and Environment of Rare-Earth Ions in Thin Oxide Films towards Wafer-Scalable Quantum Technologies</em>, Mater. Adv. <strong>3</strong>, 300 (2022). doi: 10.1039/D1MA00753J</p>
How green is my valley? Measuring open access friendliness of Indian Institutes of Technology (IITs) through data carpentry (dataset)
<p>This data set is related to the book chapter with the following bibliographic details - Mukhopadhyay, P. (2022). How green is my valley? Measuring open access friendliness of Indian Institutes of Technology (IITs) through data<br> carpentry. In A. Biswas & M. Das Biswas (Eds.), Panorama of open access: Progress, practices & prospects (1st ed., pp. 67–89). Ess Ess. https://doi.org/10.5281/zenodo.6511080.</p> <p>It includes the truncated version of the final data set that has been used for analyzing Open Access Friendliness (OAF) of the Indian Institutes of Technology (IITs). The zipped version of the data set is around 95 MB (465 MB after decompress).</p>
ICO2CHEM synthesis reactors technology
<p>This video describes the key technology behind the ICO2CHEM project. </p> <p>The shooting took place at INERATEC's lab (https://ineratec.de/en/home/). </p>
UF & UAB's Phase 2 Demonstration Study: Developing a Model to Support Transportation System Decisions considering the Experiences of Drivers of all Age Groups with Autonomous Vehicle Technology (Project A3)
<p>Enclosed you will find the data collected during our STRIDE Phase II research project (A3) and a data dictionary.</p>
The transmission of pottery technology amongst prehistoric European hunter-gatherers: code and data
<p>Included in this paper are the data files which enable the main analytical findings of the paper to be reproduced. Some aspects of the spatial-temporal modelling will heavily depend on the user’s configuration and the digital elevation model available, so intermediate data that support the main conclusions of the paper have been included in the data repository. </p>
data set of scopus about technology and halal meat supply chain publications
<p>This is the dataset of papers related to the topics of technology and halal meat supply chain collected from Scopus. The duration of year was 2008-2022</p>
Database of Rural Technological Trajectories of the Legal Amazon delimited by the Method of Differentiation and Structural Signification of Rural Production
<p>This database contains selected variables associated with the rural economic sector of the Brazilian Legal Amazon distributed at municipal level by technological trajectories (TT) – techno-productive trajectories and their technological variants (TTP) -, as defined and theoretically justified by Costa (2021, p. 217-219).</p> <p>The TTs are designed by a method that combines <em>differentiation and structural signification</em> of rural production in a given territory – hereafter, Method of Differentiation and Structural Signification of Rural Production (M-DESTRU).</p> <p><em>Structural differentiation</em> (Phase 1) is necessary because production systems activities play different roles, depending on the systems production modes and their territorial context: cattle ranching, for example, performs very different economic functions when practiced in family structures (peasants) in the municipalities of the Lower Amazonas, in comparison with wage-based farms in Southeast Pará; the roles played by temporary crops in the peasant systems of the Lower Tocantins are also quite different from those that are observed among employers' establishments in the Lower Amazon; and so on. This phase of the methodology qualifies these differences and has its procedures described on pages 441 and 442 of Costa (2021).</p> <p>In phase 2, M-DESTRU verifies how these structurally dissimilar activities, combine with others linked to the practices of the agents of each production mode, conforming convergences that result in distinct patterns. These <em>patterns</em> are semantically associated with TTs or TTPs<em> structures</em> that are in movement, and these structures all together make up for the region's rural economic system. This Phase's procedures are detailed on pages 441 and 442 of the aforementioned work.</p> <p>The territory of the Brazilian Legal Amazon encompasses 772 municipalities: all from eight states (Acre, Amapá, Amazonas, Mato Grosso, Pará, Rondônia, Roraima and Tocantins) and part of the State of Maranhão (west of the 44ºW meridian).</p> <p>The base data are from the Brazilian Institute of Geography and Statistics (IBGE), from the 1995, 2006 and 2017 Agricultural Censuses. The credit data for 2017 are from the Central Bank of Brazil.</p> <p>The dataset is organized as: Zen1995_LegalAmazon_Inicial.csv; Zen2006_LegalAmazon_Inicial.csv and Zen2017_LegalAmazon_Inicial.csv. In each table the column names are self-explanatory.</p> <p> </p> <p>Reference:</p> <ul> <li>Costa FA. 2021. Structural diversity and change in rural Amazonia: A comparative assessment of the technological trajectories based on agricultural censuses (1995, 2006 and 2017). Nova Economia 31(2). <p> </p> <p> </p> <p> </p> </li> </ul>
Machine Learning Enabled Multi-Radio Access Technology Selection in 5G Networks
<p>In this paper, we present a machine learning algorithm for effective RAT selection in 5G networks by considering the geo-location (latitude and longitude) of the user as well as the received signal strength intensity (RSSI) from the base station as basic parameters, real live data from a 5G network base-station were collated, divided into training and testing data-sets, the training data-sets (input) were used to train models of supervised machine learning classification algorithm: Decision Tree (DT), Extra Tree (XTREE), Random Forest (RF), Gradient Boosting (GB), and eXtreme Gradient Boosting (XGBoost); these trained models are further tested with input test data-sets to predict/select the appropriate RAT (4G/5G) as labelled output. Evaluation of results showed a measure of accuracy of our chosen model of RAT selection; (XGBoost) at optimal level 93.86\%, which was further cross validated at 92.9\% when compared with other algorithms for its effectiveness on future data and mitigation ability on over-fitting and under-fitting issues, hence recommended for planning and optimization purposes in similar urban/dense-urban environment to assist in maintaining the rapidly increasing demand of network connections and devices.</p>
Negative emissions technologies and pathways database
<p>This database has been developed to support mathematical analyses of different greenhouse gas removal technologies and their associated deployment implications. The technologies considered in this dataset includes bioenergy with CO2 capture and storage (BECCS), direct air capture and storage (DACCS), afforestation (AF), and enhanced weathering (EW) as they are a representative set of the most commonly reported negative emissions technologies. </p> <p> </p>
Figure 1. The component elements and technological dimension of private life-Educational Research on the Technological Dimension of Private Life
<p>At the level of each component element there are ten principal/main dimensions:<br> objective, biological, psycho-social, esthetic, religious, technological, economical, historical,<br> cultural - political, juridical. The analysis of all the aspects presented above offers a holistic<br> view on the concept of private life, a view that allows a complete representation of all the<br> components. We plot the dynamic structure of the components of privacy by means of two<br> axes, in which the vertical scale is characteristic and essential elements horizontally (Figure<br> 1).</p>
Figure 3. The graphic representation of the frequencies of the specifications for the technological dimension in curricular documents--Educational Research on the Technological Dimension of Private Life
<p>There are also major differences between schooling levels (Figure 3). In the<br> gymnasium educational system (12) and high school educational system (15) there are more<br> themes concerned with the technological dimension at the level of school curricula, as<br> compared to the primary educational system (2). Also, in alternative textbooks, these themes<br> are predominant in the high school educational system (56) and the gymnasium educational<br> system (27), as compared to the primary educational system (1).</p>
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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.