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6,467 results for “process”
The Role of Artificial Intelligence in The Architectural Design Process
<p>This research explores the integration of artificial intelligence (AI) in architectural design education, focusing on its impact on student creativity and design processes. Architects have increasingly adopted AI, with many acknowledging its potential to enhance efficiency. However, challenges remain, particularly in balancing AI with human creativity and intuition. The study, conducted in a third-year architecture studio course, employs Quantitative Content Analysis (QCA) to examine how AI influences design outcomes. By analyzing student reports, the research investigates patterns in AI usage and its role in achieving educational goals, aiming to inform AI's integration into architecture pedagogy.</p>
CORDIS H2020 PROJECT DATA PROCESSED
<p>Dataset about research projects funded under the programme Horizon 2020, from 2014 to 2020. Each row corresponds to the participation of a specific organisation in a specific research project.</p>
CORDIS H2020 PROJECT DATA PROCESSED TEXT
<p>Dataset about research projects funded under the programme Horizon 2020, from 2014 to 2020. Each text file corresponds to a specific research project (grant agreement).</p>
Harnessing Natural Language Processing to Deciphers Ancient Temple Texts
<p>This paper explores the use of Natural Language Processing (NLP) and Optical Character Recognition (OCR) to digitize and translate ancient Indian temple inscriptions. By applying machine learning to ancient languages like Sanskrit and Tamil, the research offers methods to decode and reconstruct eroded texts. Semantic analysis tools are also used to uncover the cultural and historical significance of the inscriptions. This interdisciplinary approach combines data science with cultural preservation, providing new ways to understand ancient texts.</p>
USING THE NATIONAL TRADITIONS OF THE UZBEK PEOPLE IN THE PROCESS OF SECONDARY SCHOOL STUDENTS
Open the record for dataset details and reuse information.
Social participation of young people in university settings: gender gap in participatory profiles and explanatory processes.
<p>The present research aimed to analyze, on the one hand, the participatory profile of women and men considering the influence of higher education and, on the other hand, to determine to what extent gender can moderate the impact of intrapersonal variables that previous research has shown to be related to social participation. To this end, two studies are presented: Study 1 addresses the analysis of 3856 university students who had completed at least their first academic year (DATABASE.UNIVERSITY). Study 2 involved 1061 young people who have just started their university studies (DATABASE.STARTERS). In both cases an online questionnaire was used to assess different forms of social participation and the intrapersonal variables of internal efficacy, moral obligation, self-concept and social norms. </p>
Pre-processed data and trained model weight in ADAF
<h1>Pre-proccesd data</h1> <p>The pre-proccesd data consists of input-target pairs. The inputs include surface weather observations within a 3-hour window, GOES-16 satellite imagery within a 3-hour window, HRRR forecast, and topography. The target is a combination of RTMA and surface weather observations. The table below summarizes the input and target datasets utilized in this study. All data were regularized to grids of size 512 $\times$ 1280 with a spatial resolution of 0.05 $\times$ 0.05 $^\circ$. </p> <table> <tbody> <tr> <td> </td> <td><strong>Dataset</strong></td> <td><strong>Source</strong></td> <td><strong>Time window</strong></td> <td><strong>Variables/Bands</strong></td> </tr> <tr> <td><strong>Input</strong></td> <td>Surface weather observations</td> <td>WeatherReal-Synoptic (Jin et al., 2024)</td> <td>3 hours</td> <td>Q, T2M, U10, V10</td> </tr> <tr> <td><strong>Input</strong></td> <td>Satellite imagery</td> <td>GOES-16 (Tan et al., 2019)</td> <td>3 hours</td> <td>0.64, 3.9, 7.3, 11.2 $\mu m$</td> </tr> <tr> <td><strong>Input</strong></td> <td>Background</td> <td>HRRR forecast (Dowell et al., 2022)</td> <td>N/A</td> <td>Q, T2M, U10, V10</td> </tr> <tr> <td><strong>Input</strong></td> <td>Topography</td> <td>ERA5 (Hersbach et al., 2019)</td> <td>N/A</td> <td>Geopotential</td> </tr> <tr> <td><strong>Target</strong></td> <td>Analysis</td> <td>RTMA (Pondeca et al., 2011)</td> <td>N/A</td> <td>Q, T2M, U10, V10</td> </tr> <tr> <td><strong>Target</strong></td> <td>Surface weather observations</td> <td>WeatherReal-Synoptic (Jin et al., 2024)</td> <td>N/A</td> <td>Q, T2M, U10, V10</td> </tr> </tbody> </table> <p><a href="https://zenodo.org/api/records/14020879/draft/files/2022-10-01_06.nc/content" target="_blank" rel="noopener noreferrer">2022-10-01_06.nc</a> is a sample of pre-proccesd data. The vairables in this file contain the input-target pairs mentioned above.</p> <div> <p>A sample file contains the following variables:</p> <table> <tbody> <tr> <td><strong>Variable</strong></td> <td><strong>Decription</strong></td> <td><strong>Dimension</strong></td> </tr> <tr> <td>z</td> <td>Topography, normalized</td> <td>[lat, lon]</td> </tr> <tr> <td>rtma_t</td> <td>T2M from RTMA, normalized</td> <td>[lat, lon]</td> </tr> <tr> <td>rtma_q</td> <td>Q from RTMA, normalized</td> <td>[lat, lon]</td> </tr> <tr> <td>rtma_u10</td> <td>U10 from RTMA, normalized</td> <td>[lat, lon]</td> </tr> <tr> <td>rtma_v10</td> <td>V10 from RTMA, normalized</td> <td>[lat, lon]</td> </tr> <tr> <td>sta_t</td> <td>T2M from station's observation, 0 means non-station, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>sta_q</td> <td>Q from station's observation, 0 means non-station, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>sta_u10</td> <td>U10 from station's observation, 0 means non-station, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>sta_v10</td> <td>V10 from station's observation, 0 means non-station, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>CMI02</td> <td>ABI Band 2: visible (red), normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>CMI07</td> <td>ABI Band 7: shortwave infrared, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>CMI10</td> <td>ABI Band 10: low-level water vapor, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>CMI14</td> <td>ABI Bands 14: longwave infrared, normalized</td> <td>[obs_time_window, lat, lon]</td> </tr> <tr> <td>hrrr_t</td> <td>T2M from HRRR 1-hour forecast</td> <td>[lat, lon]</td> </tr> <tr> <td>hrrr_q</td> <td>Q from HRRR 1-hour forecast</td> <td>[lat, lon]</td> </tr> <tr> <td>hrrr_u_10</td> <td>U10 from HRRR 1-hour forecast</td> <td>[lat, lon]</td> </tr> <tr> <td>hrrr_v_10</td> <td>V10 from HRRR 1-hour forecast</td> <td>[lat, lon]</td> </tr> </tbody> </table> <p> </p> </div> <h1>Pre-comuted normalization statistics</h1> <p><a href="https://zenodo.org/api/records/14020879/draft/files/stats.csv/content" target="_blank" rel="noopener noreferrer">stats.csv</a> is pre-comuted normalization statistics.</p> <h1>Pre-trained model weights</h1> <p><a href="https://zenodo.org/api/records/14020879/draft/files/best_ckpt.tar/content" target="_blank" rel="noopener noreferrer">best_ckpt.tar</a> is the pre-trained model weights.</p> <h1>References</h1> <ol> <li>Jin, W. et al. WeatherReal: A Benchmark Based on In-Situ Observations for Evaluating Weather Models. (2024).</li> <li>Dowell, D. et al. The High-Resolution Rapid Refresh (HRRR): An Hourly Updating Convection-Allowing Forecast Model. Part I: Motivation and System Description. Weather and Forecasting 37, (2022).</li> <li>Tan, B., Dellomo, J., Wolfe, R. & Reth, A. GOES-16 and GOES-17 ABI INR assessment. in Earth Observing Systems XXIV vol. 11127 290–301 (SPIE, 2019).</li> <li>Hersbach, H. et al. ERA5 monthly averaged data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) 10, 252–266 (2019).</li> <li>Pondeca, M. S. F. V. D. et al. The Real-Time Mesoscale Analysis at NOAA’s National Centers for Environmental Prediction: Current Status and Development. Weather and Forecasting 26, 593–612 (2011).</li> </ol>
Data from: Concurrent temporal channels for auditory processing: oscillatory neural entrainment reveals segregation of function at different scales
Natural sounds convey perceptually relevant information over multiple timescales, and the necessary extraction of multi-timescale information requires the auditory system to work over distinct ranges. The simplest hypothesis suggests that temporal modulations are encoded in an equivalent manner within a reasonable intermediate range. We show that the human auditory system selectively and preferentially tracks acoustic dynamics concurrently at 2 timescales corresponding to the neurophysiological theta band (4–7 Hz) and gamma band ranges (31–45 Hz) but, contrary to expectation, not at the timescale corresponding to alpha (8–12 Hz), which has also been found to be related to auditory perception. Listeners heard synthetic acoustic stimuli with temporally modulated structures at 3 timescales (approximately 190-, approximately 100-, and approximately 30-ms modulation periods) and identified the stimuli while undergoing magnetoencephalography recording. There was strong intertrial phase coherence in the theta band for stimuli of all modulation rates and in the gamma band for stimuli with corresponding modulation rates. The alpha band did not respond in a similar manner. Classification analyses also revealed that oscillatory phase reliably tracked temporal dynamics but not equivalently across rates. Finally, mutual information analyses quantifying the relation between phase and cochlear-scaled correlations also showed preferential processing in 2 distinct regimes, with the alpha range again yielding different patterns. The results support the hypothesis that the human auditory system employs (at least) a 2-timescale processing mode, in which lower and higher perceptual sampling scales are segregated by an intermediate temporal regime in the alpha band that likely reflects different underlying computations.
Combining process-based and data-driven approaches to forecast dune and beach change Data
<p>Data and code associate with "Combining process-based and data-driven approaches to forecast dune and beach change Data" manuscript</p>
Table S1 Full factorial combination of laser process parameters and experimental results.
<p>This dataset is experimental results from a research topic of Prediction and optimization of geometrical quality for pulsed laser cutting of non-oriented electrical steel sheet.</p>
Figure 5 from: Remsen D, Knapp S, Georgiev T, Stoev P, Penev L (2012) From text to structured data: Converting a word-processed floristic checklist into Darwin Core Archive format. PhytoKeys 9: 1-13. https://doi.org/10.3897/phytokeys.9.2770
Figure 5 - Example of a scientific name entry.
Figure 1 from: Remsen D, Knapp S, Georgiev T, Stoev P, Penev L (2012) From text to structured data: Converting a word-processed floristic checklist into Darwin Core Archive format. PhytoKeys 9: 1-13. https://doi.org/10.3897/phytokeys.9.2770
Figure 1 - A typical species record from the checklist.
Figure 4 from: Remsen D, Knapp S, Georgiev T, Stoev P, Penev L (2012) From text to structured data: Converting a word-processed floristic checklist into Darwin Core Archive format. PhytoKeys 9: 1-13. https://doi.org/10.3897/phytokeys.9.2770
Figure 4 - Data correctly aligned with columns.
Figure 6 from: Remsen D, Knapp S, Georgiev T, Stoev P, Penev L (2012) From text to structured data: Converting a word-processed floristic checklist into Darwin Core Archive format. PhytoKeys 9: 1-13. https://doi.org/10.3897/phytokeys.9.2770
Figure 6 - Synonym records (highlighted) added and linked.
Figure 2 from: Remsen D, Knapp S, Georgiev T, Stoev P, Penev L (2012) From text to structured data: Converting a word-processed floristic checklist into Darwin Core Archive format. PhytoKeys 9: 1-13. https://doi.org/10.3897/phytokeys.9.2770
Figure 2 - Taxon records imported into a database.
Figure 3 from: Mankga L, Kowiyou Y, Moteetee A, Daru B, van der Bank M (2013) Efficacy of the core DNA barcodes in identifying processed and poorly conserved plant materials commonly used in South African traditional medicine. ZooKeys 365: 215-233. https://doi.org/10.3897/zookeys.365.5730
Figure 3 - Continued.
Figure 2 from: Véron R, Fernando N, Narayanan N, Upreti B, Ambat B, Pallawala R, Rajbhandari S, Rao Dhananka S, Zurbrügg C (2018) Social processes in post-crisis municipal solid waste management innovations: A proposal for research and knowledge exchange in South Asia. Research Ideas and Outcomes 4: e31430. https://doi.org/10.3897/rio.4.e31430
Figure 2 Partly decentralized waste chains in Kerala.
Figure 1 from: Véron R, Fernando N, Narayanan N, Upreti B, Ambat B, Pallawala R, Rajbhandari S, Rao Dhananka S, Zurbrügg C (2018) Social processes in post-crisis municipal solid waste management innovations: A proposal for research and knowledge exchange in South Asia. Research Ideas and Outcomes 4: e31430. https://doi.org/10.3897/rio.4.e31430
Figure 1 Schematic representation of the institutional MSWM architecture.
Single-Cell RNA-seq Uncovers Dynamic Processes Orchestrated by RNA-Binding Protein DDX43 in Chromatin Remodeling during Spermiogenesis
<p>All datasets include analyzed scRNA-seq, peak files called from eCLIP-seq datasets, and spliced/unspliced readouts of single cell.</p>
Mapping of Use Cases to ISO 12207 Processes
<p>Mapping of software analytics use cases to software life cycle processes from ISO/IEC/IEEE 12207:2017 (IEEE Access paper: On Adopting Software Analytics for Managerial Decision-Making: A Practitioner's Perspective).</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.