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Dataset results
91 results for “training methods”
SURFBIO Training: "Analytical methods for the study of microbial cell-Surface and Surface-colloid interactions" (2021)
<p>2021. SURFBIO project training within WP1.</p><p>Content:</p><ul><li><strong>Webinar on Vertical scanning interferometry: a microscopic technique to analyze surface reactivity, </strong>by Dr. Cornelius Fischer (HZDR, Germany) </li><li><strong>Webinar on An introduction to radiolabelling as a versatile tool in colloid tracing, </strong>by Stefan Schymura (HZDR, Germany).</li><li><strong>Webinar on Development and construction of biocarriers and aggregates for potential industrial applications</strong>, by Dr. Andre Skirtach and Dr. Bogdan Parakhonskiy (GHENT University). </li><li><strong>Materials and fluidic design to study artificially functionalized microorganisms</strong>, by Dr. Andre Skirtach and Dr. Bogdan Parakhonskiy (GHENT University). </li></ul>
Training sessions for innovation procurers: methods lessons learned, best practices
<p>These webinars explore practical methods and key lessons learned from implementing innovation procurement, featuring insights from the BUILD project. Using real-world case studies from municipalities across Europe, they highlight effective strategies for overcoming procurement challenges and fostering collaboration between public buyers and the market. The sessions are ideal for public procurement professionals, policymakers, and stakeholders seeking actionable guidance on leveraging procurement as a tool for innovation and sustainability.<br><br>They are available here:<br><br><a href="https://www.youtube.com/watch?v=v-9blcYpK_g">Public procurers’ exchange of best practices - Innovation Procurement Task Force webinar insights</a></p> <p><a href="https://www.youtube.com/watch?v=EY759zLpmdU">Innovation Procurement Webinar: Methods and lessons learned by real cases - the BUILD Project</a></p>
METHODS OF TRAINING AND ADAPTATION OF AI AGENTS IN COMPLEX PROCESS CONTROL SYSTEMS
<p>The article presents a study of modern methods of training and adaptation of artificial agents used in managing complex processes, which are characterized by a high level of uncertainty and the need for prompt response to changes. Key methodological approaches such as machine learning and neuroevolution are discussed. These approaches allow AI agents to accumulate knowledge about the behavior of systems continuously, analyze external changes, and adjust the management strategy depending on environmental conditions, which significantly increases their ability to predict and prevent possible failures in management.</p> <p>In the course of the study, models were considered that allow automating the execution of complex, multitasking processes, minimizing human intervention, and reducing the likelihood of errors. In addition, the presented methods provide high flexibility and scalability of systems, which is especially important in industrial and technological industries, where stability and reliability are critical. The results showed that AI agents with adaptive learning capabilities can increase operational efficiency while reducing costs and optimizing resource use. The conclusion highlights the prospects of using artificial intelligence to build highly autonomous control systems capable of responding to dynamic challenges, which opens up new horizons for automation and intellectual support in industrial production, logistics, and other key areas.</p> <p>Thus, the article makes a significant contribution to understanding the role of AI in management modernization, offering practical recommendations on the implementation of intelligent agents in real-world scenarios to increase productivity and sustainability.</p>
Galaxy Training Data for "Designing plasmids encoding predicted pathways by using the BASIC assembly method"
<p>This dataset provides the data needed for the Galaxy BASIC assembly workflow training tutorial (<a href="https://galaxy-synbiocad.org">https://galaxy-synbiocad.org</a>). This workflow provides a pathway to design plasmids encoding predicted metabolic pathways using the BASIC assembly method (<a href="https://doi.org/10.1021/sb500356d">https://doi.org/10.1021/sb500356d</a>). It generates scripts allowing the automatic construction of these plasmids using an Opentrons liquid handling robot. After downloading these scripts on a computer connected to an Opentrons (<a href="https://opentrons.com">https://opentrons.com</a>), the user can perform the automatic construction of the plasmids on the bench.</p> <p>The content of the dataset is as follows:</p> <ul> <li> <p>an SBML file modeling a heterologous pathway producing lycopene such as those produced by the Pathway Analysis Workflow (<a href="https://galaxy-synbiocad.org">https://galaxy-synbiocad.org</a>).</p> </li> <li> <p>a CSV file listing the parts to be used (linkers, backbone and promoters) in the constructions.</p> </li> <li> <p>two YAML files providing two examples of settings, i.e. providing the identifiers of the laboratory equipment and the parameters of the DNA robot.</p> </li> </ul>
Training data for the component contribution method
<p>Data files containing measured Gibbs free energies of reaction and formation (or electrostatic potential). This data is used by the component-contribution method to train the regression model that is used to estimate Gibbs free energies of many other biochemical reactions.</p>
Data Set on Content Excerpts from Relevant Literature for a Scoping Review of Evacuation Training Methods in Buildings
<p>This Excel-file contains a set of data from a scoping review on methods for fire evacuation training in buildings. The review follows the PRISMA approach (Transparent Reporting of Systematic Reviews and Meta-Analyses) and systematically identifies 73 sources among scientific literature published between 1997 and 2022. The dataset contains information excerpted through a custom template on the employed training methods and technology, study information, participants, and contents of the discussion of the 73 sources of evidence that were identified in the systematic review process.</p>
Data Set on the Literature Screening for a Scoping Review of Evacuation Training Methods in Buildings
<p>This data set contains all retrieved literature records of a scoping review on fire evacuation training methods in buildings together with the reasoning for their in- or exclusion in the review.</p>
Training set of microscopy images for Park et al. Nature Methods 2023
<p>All data for key-point tracking:</p><p>keypoint-tracking-data-*.h5</p><p>Videos illustrating key-point tracking:</p><p>keypoint-tracking-videos.zip</p><p>Sample data for 3D volume tracking and videos:</p><p>volumetric-tracking-data-and-videos.zip</p>
Dataset for testing and training map-matching methods
<p>We present a dataset for testing, bench-marking, and offline learning of map-matching algorithms. For the first time, a large enough dataset is available to prove or disprove map-matching hypotheses on a world-wide scale. There are several hundred map-matching algorithms published in literature, each tested only on a limited scale due to difficulties in collecting truly large scale data. Our contribution aims to provide a convenient gold standard to compare various map-matching algorithms between each other. Moreover, as many state-of-the-art map-matching algorithms are based on techniques that require offline learning, our dataset can be readily used as the training set. Because of the global coverage of our dataset, learning does not have to be be biased to the part of the world where the algorithm was tested.</p>
PMF-LP: the first 10 m plastic-mulched farmland distribution map (2019-2021) in the Loess Plateau of China generated using training sample generation and classifier transfer method
<p>This dataset provides 10-m resolution plastic-mulched farmland distribution map in the Loess Plateau of China from 2019 to 2021</p> <p>*** The data file is in ".tif" format</p> <p>*** Temporal resolution: Annually</p> <p>*** Temporal coverage: 2019-2021</p> <p>*** Pixel size: 10 m</p> <p>*** Projection information: EPSG: 4326</p> <p>*** Values: 1 denotes plastic-mulched farmland (PMF) and 0 denotes non-plastic-mulched farmland (non-PMF)</p>
Examining Rehabilitation Training Methods
ClinicalTrials.gov study NCT02766400. IPD Sharing: NO. Countries: 1. Publications: 2.
Ultrasound-guided Nerve Block Training Model and Evaluation Method
ClinicalTrials.gov study NCT06219005. IPD Sharing: NO. Countries: 1. Publications: 2.
Caregiver Skills Training: Comparing Clinician Training Methods
ClinicalTrials.gov study NCT06038799. IPD Sharing: YES. Countries: 1. Publications: 5.
Comparing Two Training Methods for Opioid Wizard
ClinicalTrials.gov study NCT04867382. IPD Sharing: YES. Countries: 1. Publications: 1.
Interpretation Training to Reduce Anxiety: Evaluating Technology-based Delivery Models and Methods to Reduce Attrition
ClinicalTrials.gov study NCT03498651. IPD Sharing: YES. Countries: 1. Publications: 1.
Statistical model training data for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"
<p>Gzipped CSV files containing convection scheme inputs and outputs used for training.</p> <p>Column format of each file:</p> <p>THETA_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,Q_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DTHETA_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DQ_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28</p> <p>where THETA_IN are input values of potential temperature [K], Q_IN are input values of specific humidity [kg/kg], DTHETA are changes in potential temperature due to convection [K], DQ are changes in specific humidity due to convection [kg/kg].</p> <p>Key:</p> <p>"llcs" are simulations with Lambert-Lewis.</p> <p>"gr" are simulations with Gregory-Rowntree.</p> <p>"4xco2" have 4 x pre-industrial atmospheric carbon dioxide concentration. (Others have 1 x pre-industrial atmospheric carbon dioxide concentration.)</p> <p>"rh0.7" and "rh0.9" have LLCS RHCRIT set to 70% and 90% respectively.</p> <p>All are 30 day simulations either for January "jan" or July "jul".</p> <p> </p>
Dataset of use, acceptance, attitude and training needs in active method-ologies of Spanish teachers.
<p>Dataset collected for the purpose of analyzing various aspects related to the implementation of active methodologies (AM) in the educational field in Spain. This dataset includes data on: Use of active methodologies: information on the frequency and the way in which teachers use different active methodologies in their classes, such as cooperative learning, project-based learning, challenge-based learning, among others. Student acceptance: evaluation of how students perceive and respond to active methodologies, observing their willingness to participate and the benefits they consider derived from these methodologies. Teacher attitudes: Teachers' opinions and perceptions about active methodologies, including their effectiveness, advantages, and difficulties in implementing them in the classroom. Training needs: information on teachers' perceptions about the need for training in the use of active methodologies, particularly in emerging areas such as the use of digital technologies in the classroom, and the preparation to implement these strategies more effectively.</p>
Training dataset for the TRENDY method
<p>This dataset is used for training the TRENDY method for gene regulatory network inference. It also contains the SINC test data set.</p> <p>For a brief description of the code for TRENDY method, see https://github.com/YueWangMathbio/TRENDY. </p> <p>See https://github.com/YueWangMathbio/TRENDY/blob/main/GRN_transformer.pdf for the manuscript of TRENDY method.</p> <p> </p> <p>To use the data:</p> <p>1 download all files from https://github.com/YueWangMathbio/TRENDY</p> <p>2 download all files from this database (both https://zenodo.org/records/14927741 and https://zenodo.org/records/13929908)</p> <p>3 in the folder with all files from GitHub, creat a folder named "total_data_10", and unzip all files with name "dataset....zip" in this folder</p> <p>4 unzip "rev_wendy_all_10.zip" in the folder with all files from GitHub</p> <p>5 unzip "SINC_data.zip", and the files into the folder "SINC"</p> <p> </p> <p>The "total_data_10" folder will contain 102 groups of data, where each group has eight files with different name endings:</p> <p>xxx_A: 1000 ground truth gene regulatory networks, each of size 10*10</p> <p>xxx_cov: 11000 covariance matrices for 1000 samples at 11 time points, each of size 10*10</p> <p>xxx_data: 1000 gene expression samples, each of size 100*10*11 (100 cells, 10 genes, 11 time points)</p> <p>xxx_genie: 10000 inferred gene regulatory networks by GENIE3 method for 1000 samples at 10 time points, each of size 10*10</p> <p>xxx_nlode: 1000 inferred gene regulatory networks by NonlinearODEs method for 1000 samples, each of size 10*10</p> <p>xxx_revcov: 10000 constructed pseudo covariance matrices for 1000 samples at 10 time points, each of size 10*10</p> <p>xxx_sinc:1000 inferred gene regulatory networks by SINCERITIES method for 1000 samples, each of size 10*10</p> <p>xxx_wendy: 10000 inferred gene regulatory networks by WENDY method for 1000 samples at 10 time points, each of size 10*10</p> <p> </p> <p>The "rev_wendy_all_10" folder will contain two groups of data, where each group has eight files with different name endings:</p> <p>xxx_ktstar: 10000 inferred covariance matrices by the first half of TRENDY for 1000 samples at 10 time points, each of size 10*10</p> <p>xxx_revwendy: 10000 inferred gene regulatory networks by the first half of TRENDY for 1000 samples at 10 time points, each of size 10*10</p> <p> </p> <p>The first 100 group with numbering are for training. The one group with "val" is for validation. The one group with "test" is for testing.</p> <p> </p> <p>If you want to train or test new GRN inference methods, then just use the xxx_A files and xxx_data files.</p> <p> </p> <p> </p>
Objective Assessment of Live-streaming Vs Face-to-face Teaching Method Effectiveness in Training Simple Wound Suturing Skill to Surgical Clerkship
<p><strong>Objective Assessment of Live-streaming Vs Face-to-face Teaching Method Effectiveness in Training Simple Wound Suturing Skill to Surgical Clerkship </strong></p>
Face to Face vs. Group Training Methods on Pulse Rate Taking in Patients With Cardiovascular Diseases.
ClinicalTrials.gov study NCT01988064. IPD Sharing: Not stated. Countries: 1. Publications: 3.
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.