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6,623 results for “generalization”
Udayagiri, Madhya Pradesh. General plan of the site.
<p>Udayagiri, Madhya Pradesh. General plan of the site, showing configuration of the hill, the location of principal caves, temples and main tanks.</p>
བསམ་ཡས་ central Tibet, general view from Ha po ri.
<p>Samyas (བསམ་ཡས་) central Tibet, general view from Ha po ri, photograph prior to modern extension and development.</p> <p><a href="https://catalog.lib.uchicago.edu/vufind/Alphabrowse/Home?source=topic&from=Tibet+Region--History.">Tibet Region -- History.</a><br> <a href="https://catalog.lib.uchicago.edu/vufind/Alphabrowse/Home?source=topic&from=Tibet+Region--Civilization.">Tibet Region -- Civilization.</a></p> <p><a href="https://catalog.lib.uchicago.edu/vufind/Alphabrowse/Home?source=topic&from=Samye+%28Monastery+%3A+Zhanang+Xian%2C+China%29--History.">Samye (Monastery : Zhanang Xian, China) -- History.</a><br> <a href="https://catalog.lib.uchicago.edu/vufind/Search/Results?lookfor=Samye+%28Monastery+%3A+Zhanang+Xian%2C+China%29&type=Subject">Samye (Monastery : Zhanang Xian, China)</a><br> <a href="https://catalog.lib.uchicago.edu/vufind/Alphabrowse/Home?source=topic&from=Buddhist+monasteries--China--Zhanang+Xian--History.">Buddhist monasteries -- China -- Zhanang Xian -- History</a></p> <p> </p>
Bhagdei (जामगढ़-भगदेई, Raisen district), Madhya Pradesh. General view of ruined shrine.
<p>Bhagdei (भगदेई, Raisen district), Madhya Pradesh. General view of ruined shrine to the east of the village at the end of the dam. Dedicated to Hanumān with supplementary images of Durgā, circa 12th century. Temple ruin located at <a href="http://wikimapia.org/#lang=en&lat=23.108137&lon=78.255208&z=19&m=bs&show=/36622703/%E0%A4%AD%E0%A4%97%E0%A4%A6%E0%A5%87%E0%A4%88-Bhagdei-ruins-of-Hanum%C4%81n-temple&search=Bhagdei">23°6'28"N 78°15'22"E</a>. Photograph October, 2008.</p>
Salihundam, Gara Mandal, Srikakulam district, Andhra Pradesh. General view of five votive inscriptions.
<p>Salihundam, Gara Mandal, Srikakulam district, Andhra Pradesh. Five votive inscriptions at Buddhist site, incised on a semi-circular slab once marking the stepped entrance to a shrine. Located approximately at 18°20'7"N 84°2'28"E, as documented in 2007.</p>
Udayagiri, Madhya Pradesh. Cave 6, general view.
<p>Udayagiri, Madhya Pradesh. Cave 6, general view, showing relief panels with Gaṇeśa, Viṣṇu, door guardians, Durgā and Śiva, probably 5th century. As documented in 2009.</p>
Udayagiri, Madhya Pradesh. Cave 7, general view from south.
<p>Udayagiri, Madhya Pradesh. Cave 7, general view from south, showing damaged mother-goddesses in the cave niche, flanked externally by Kārttikeya and Gaṇeśa, now much eroded, probably 5th century. As documented in 11/2007.</p>
Udayagiri, Madhya Pradesh. Cave 7, general view.
<p>Udayagiri, Madhya Pradesh. Cave 7, general view, showing damaged mother-goddess figures, flanked by niches with Gaṇeśa and Kārttikeya, probably 5th century. As documented in 11/2007.</p>
New insights into the generalized Rutherford equation for nonlinear neoclassical tearing mode growth from 2D reduced MHD simulations
<p>Two dimensional reduced MHD simulations of neoclassical tearing mode growth and suppression by ECCD are performed. The perturbation of the bootstrap current density and the EC drive current density perturbation are assumed to be functions of the perturbed flux surfaces. In the case of ECCD, this implies that the applied power is flux surface averaged to obtain the EC driven current density distribution. The results are consistent with predictions from the generalized Rutherford equation using common expressions for $\Delta^\prime_{\rm bs}$ and $\Delta^\prime_{\rm ECCD}$. These expressions are commonly perceived to describe only the effect on the tearing mode growth of the helical component of the respective current perturbation acting through the modification of Ohm's law. Our results show that they describe in addition the effect of the poloidally averaged current density perturbation which acts through modification of the tearing mode stability index. Except for modulated ECCD, the largest contribution to the mode growth comes from this poloidally averaged current density perturbation.</p>
General CCU (Product) Acceptance & Trade-Off Decisions
<p>The dataset obtained for WP6 of the CO2SMOS project contains anonymized data including demographic and attitudinal information, perceptions of benefits and barriers regarding CCU adoption and acceptance data obtained from a choice-based conjoint experiment on trade-off decisions in CCU product purchase situations, obtained through an online survey conducted with participants from Germany, Norway, Poland, and Spain.</p>
BAM Generalized National Models Documentation, Version 4.0
<h2>A generalized modeling framework for spatially extensive species abundance prediction and population estimation</h2> <p><span>In the face of rapid environmental change, spatially explicit estimates of species abundance and distribution are needed to inform conservation planning and management decisions across a range of spatial scales. We present a generalized modeling framework bridging the gap between local studies and regional to national management needs by compiling and harmonizing data from many sources to predict avian abundance at a fine resolution and broad extent. We first applied detectability offsets to integrate avian point-count data from a large collection of research and monitoring projects across the entire breadth of subarctic Canada (>250,000 unique sampling locations). We then subsampled the data by two time periods and sixteen geographic regions and developed boosted regression trees to model the density of 143 boreal landbird species as a function of environmental covariates representing climate, local- (250 m) and landscape-level (up to ~1.5 km) vegetation composition, land cover, and topography. Finally, bootstrapped model predictions for each region were combined to generate predictive density maps, habitat- and region-specific density estimates, and Canada-wide population estimates. Our models estimated a total of approximately 3.56 billion breeding males (7.13 billion individuals) across subarctic Canada, with the majority breeding in boreal and hemi-boreal regions. Forest generalist species made up nearly half of this estimate (1.57 billion breeding males), followed by boreal forest specialist species (1.05 billion), habitat generalists (350 million), and species associated with eastern forests (274 million), grasslands (124 million), western forests (74.7 million), wetlands (63.5 million), and Arctic tundra (17.7 million). Introduced species comprised 48.9 million breeding males. An analysis of variable importance showed that, across species, most of the variation in bird abundance was explained by landscape-level vegetation composition, suggesting that the effect of climate on bird abundance is mostly indirect, via vegetation, but that landscape-level variables are needed to capture this variation. Model classification accuracy was highest from a habitat perspective for forest- and grassland-associated species (lowest for mountain- and urban-associated species); and for Regulidae and Phasianidae from a taxonomic perspective (lowest for Bombycillidae and Paridae). In developing these models, we created a standardized, updatable, and reproducible workflow that can be used to update these analytical products and improve their utility for conservation and management planning.</span></p> <p>This data set contains:</p> <ul> <li>Reproducible code for the modeling approach based on <https://github.com/borealbirds/LandbirdModelsV4></li> <li>Source code for the website at <https://borealbirds.github.io/> based on <https://github.com/borealbirds/borealbirds.github.io></li> <li>Data and image assets for the website based on <https://github.com/borealbirds/api></li> </ul> <p>Please note, in late March 2025, we discovered a systematic error in the offsets used in these models, and have since updated the products to correct that error. For more information, please see the <https://github.com/borealbirds/QPAD-offsets-correction> repository for further details or email <bamp@ualberta.ca> for assistance.</p>
General practice characteristics associated with life expectancy of practice populations: a cross-sectional study
<p>The dataset was used to investgate features of general practice associated with life expectancy of general practice populations in England for the period 2015-2019.</p>
Lobelia appendiculata var. gattingeri (Campanulaceae) - whole plant - in flower - general view
Image of Lobelia appendiculata var. gattingeri (Campanulaceae) - whole plant - in flower - general view
Lobelia appendiculata var. gattingeri (Campanulaceae) - whole plant - in flower - general view
Image of Lobelia appendiculata var. gattingeri (Campanulaceae) - whole plant - in flower - general view
Delphinium carolinianum ssp. calciphilum (Ranunculaceae) - whole plant - in flower - general view
Image of Delphinium carolinianum ssp. calciphilum (Ranunculaceae) - whole plant - in flower - general view
Medicago lupulina (Fabaceae) - herbaceous angiosperms - whole plant - in flower - general view
Image of Medicago lupulina (Fabaceae) - herbaceous angiosperms - whole plant - in flower - general view
Lobelia appendiculata var. gattingeri (Campanulaceae) - whole plant - in flower - general view
Image of Lobelia appendiculata var. gattingeri (Campanulaceae) - whole plant - in flower - general view
3DO Dataset | On the Generalization of WiFi-based Person-centric Sensing in Through-Wall Scenarios
<p><strong>On the Generalization of WiFi-based Person-centric Sensing in Through-Wall Scenarios</strong></p> <p>This repository contains the <strong>3DO dataset</strong> proposed in <a href="https://doi.org/10.1007/978-3-031-78354-8_13">[1]</a>.</p> <p><strong>PyTroch Dataloader</strong></p> <p>A minimal PyTorch dataloader for the 3DO dataset is provided at: <a href="https://github.com/StrohmayerJ/3DO/tree/main">https://github.com/StrohmayerJ/3DO</a></p> <p><strong>Dataset Description</strong></p> <p>The 3DO dataset comprises 42 five-minute recordings (~1.25M WiFi packets) of three human activities performed by a single person, captured in a WiFi through-wall sensing scenario over three consecutive days. Each WiFi packet is annotated with a 3D trajectory label and a class label for the activities: no person/background (0), walking (1), sitting (2), and lying (3). (<strong>Note:</strong> The labels returned in our dataloader example are walking (0), sitting (1), and lying (2), because background sequences are not used.)</p> <p>The directories <code>3DO/d1/</code>, <code>3DO/d2/</code>, and <code>3DO/d3/</code> contain the sequences from days 1, 2, and 3, respectively. Furthermore, each sequence directory (e.g., <code>3DO/d1/w1/</code>) contains a <code>csiposreg.csv</code> file storing the raw WiFi packet time series and a <code>csiposreg_complex.npy</code> cache file, which stores the complex Channel State Information (CSI) of the WiFi packet time series. (If missing, <code>csiposreg_complex.npy</code> is automatically generated by the provided dataloader.)</p> <p>Dataset Structure:</p> <p>/3DO</p> <p>├── d1 <em><-- day 1 subdirectory</em></p> <p> └── w1 <em><-- sequence subdirectory</em></p> <p> └── csiposreg.csv <em><-- raw WiFi packet time series</em></p> <p> └── csiposreg_complex.npy <em><-- CSI time series cache</em></p> <p>├── d2 <-- day 2 subdirectory</p> <p>├── d3 <-- day 3 subdirectory</p> <p> </p> <p>In [1], we use the following training, validation, and test split:</p> <table> <tbody> <tr> <td><strong>Subset</strong></td> <td><strong>Day</strong></td> <td><strong>Sequences </strong></td> </tr> <tr> <td>Train</td> <td>1</td> <td>w1, w2, w3, s1, s2, s3, l1, l2, l3</td> </tr> <tr> <td>Val</td> <td>1</td> <td>w4, s4, l4</td> </tr> <tr> <td>Test</td> <td>1</td> <td>w5 , s5, l5</td> </tr> <tr> <td>Test</td> <td>2</td> <td>w1, w2, w3, w4, w5, s1, s2, s3, s4, s5, l1, l2, l3, l4, l5</td> </tr> <tr> <td>Test</td> <td>3</td> <td>w1, w2, w4, w5, s1, s2, s3, s4, s5, l1, l2, l4</td> </tr> </tbody> </table> <p><em>w = walking, s = sitting and l= lying</em></p> <p><strong>Note: </strong>On each day, we additionally recorded three ten-minute background sequences (b1, b2, b3), which are provided as well.</p> <p> </p> <p><strong>Download and Use</strong><br>This data may be used for non-commercial research purposes only. If you publish material based on this data, we request that you include a reference to our paper <a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">[1]</a>.</p> <p><a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">[1]</a> Strohmayer, J., Kampel, M. (2025). On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios. In: Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15315. Springer, Cham. <a href="https://doi.org/10.1007/978-3-031-78354-8_13" target="_blank" rel="noopener">https://doi.org/10.1007/978-3-031-78354-8_13</a></p> <p>BibTeX citation:</p> <pre>@inproceedings{strohmayerOn2025, author="Strohmayer, Julian and Kampel, Martin",<br> title="On the Generalization of WiFi-Based Person-Centric Sensing in Through-Wall Scenarios",<br> booktitle="Pattern Recognition",<br> year="2025",<br> publisher="Springer Nature Switzerland",<br> address="Cham",<br> pages="194--211",<br> isbn="978-3-031-78354-8" }</pre>
Sheep Creek Flowlines Generalized for 1:100,000 scale Representation
<p>This dataset comprises original and simplified versions of 28 hydrographic flowline features in North Dakota, USA. The original data were derived from National Hydrography Dataset (NHD) data for the 1:24,000 Sheep Creek Dam topographic map quadrangle. Flowline features were selected for 1:100,000 scale (100k) representation using the NHD VisibilityFilter attribute and further filtered and merged to form a contiguous network. Features were then simplified for 100k representation using a combination of automated and manual operations. The dataset was created for the 24<sup>th</sup> ICA Workshop on Map Generalisation and Multiple Representation; further details can be found in the workshop abstract. The dataset is intended to serve as a benchmark for cartographic generalization algorithms used to simplify and smooth hydrographic flowline features. </p> <p><strong>Statistics:</strong></p> <p>Original vertices: 9620</p> <p>Simplified vertices: 1485</p> <p>Vertex reduction: 84.6%</p> <p> </p> <table> <tbody> <tr> <td> <p> </p> </td> <td> <p><strong>Producer's Modified Hausdorff Distance (m)</strong></p> </td> <td> <p><strong>Producer's Average Distance (Vertex Influence Method)</strong></p> </td> <td> <p><strong>Sinuosity Reduction (%)</strong></p> </td> <td> <p><strong>Average Angular Deflection (degrees)</strong></p> </td> <td> <p><strong>Max Angular Deflection (degrees)</strong></p> </td> </tr> <tr> <td> <p><strong>AVG</strong></p> </td> <td> <p><strong>24.08</strong></p> </td> <td> <p><strong>4.75</strong></p> </td> <td> <p><strong>5.23</strong></p> </td> <td> <p><strong>29.61</strong></p> </td> <td> <p><strong>46.55</strong></p> </td> </tr> <tr> <td> <p><strong>MIN</strong></p> </td> <td> <p><strong>0.00</strong></p> </td> <td> <p><strong>0.00</strong></p> </td> <td> <p><strong>-0.49</strong></p> </td> <td> <p><strong>5.23</strong></p> </td> <td> <p><strong>5.23</strong></p> </td> </tr> <tr> <td> <p><strong>MAX</strong></p> </td> <td> <p><strong>38.88</strong></p> </td> <td> <p><strong>14.56</strong></p> </td> <td> <p><strong>30.40</strong></p> </td> <td> <p><strong>33.97</strong></p> </td> <td> <p><strong>53.80</strong></p> </td> </tr> </tbody> </table>
Visual-Evoked Potential (VEP) Event-Related Files from the General Anesthesia and Brain Activity (GABA) Study and Infant Sibling Project (ISP)
<p>HAPPE+ER software was optimized for developmental data using a subset of EEG files from 4-month and 10-month old infants in the General Anesthesia and Brain Activity (GABA) Study. While medically necessary, 1-2 million infants each year undergo general anesthesia – a process that sedates brain activity and impacts early sensory experiences during a time typically characterized by rapid neurocognitive development. The GABA study examines sensory and socioemotional neurodevelopment longitudinally from infancy through childhood in individuals who have and who have never undergone general anesthesia during different windows in the first year of life. The GABA study was carried out in accordance with the recommendations of the Institutional Review Board at Boston Children’s Hospital. All caregivers provided assent for their child’s participation in the GABA study and for the release of the deidentified data. </p> <p>To facilitate the use and understanding of HAPPE+ER software, we have provided a subset of the validation files from the GABA study to serve as a tutorial dataset for how to run event-related potential (ERP) data through this automated processing pipeline. Five files (a.raw - e.raw) are from four 4-month and one 10-month old infants during a pattern reversal visual-evoked potential (VEP) paradigm. Pattern reversal occurred every 500 milliseconds. The pattern stimulus onset is indicated in each file by the code: vep+. Data was collected using a 128-channel EGI HydroCel Geodesic Sensor Net and EGI Net Amps 400, sampled at 1000Hz with an online reference to channel CZ. </p> <p>We have also included a subset of files from the Infant Sibling Project (ISP), an investigation examining infants at high versus low familial risk for autism spectrum disorder over the first 3 years of life. Baseline EEG data was collected while a young child sat in a parent’s lap watching a research assistant blow bubbles or show toys for several minutes. The Infant Sibling Project was carried out in accordance with the recommendations of the Institutional Review Board at Boston University and Boston Children’s Hospital (#X06-08-0374), with written informed consent from all caregivers prior to their child’s participation in the study. All files here have been deidentified, including alteration of exact acquisition dates. Acquisition times have not been altered. For additional information about data collection paradigms, and sample studies published on the larger ISP data set, please see the following references:</p> <ol> <li>Levin, A. R., Varcin, K. J., O’Leary, H. M., Tager-Flusberg, H., and Nelson, C. A. (2017). EEG power at 3 months in infants at high familial risk for autism. J. Neurodev. Disord. 9, 1–13.</li> <li>Gabard-Durnam, L.J., Wilkinson, C., Kapur, K. et al. Longitudinal EEG power in the first postnatal year differentiates autism outcomes. Nat Commun 10, 4188 (2019). <a href="https://doi.org/10.1038/s41467-019-12202-9">https://doi.org/10.1038/s41467-019-12202-9</a></li> </ol> <p>Here we provide a subset of the full dataset with a simulated VEP signal added into the data, as example files for HAPPE+ER. To create these files, we selected a subset of 39 spatially-distributed channels in the baseline EEG files and created sixteen 30-second files using continuous segments of relatively artifact-free (clean) baseline data from the full-length files. Next, from 30-second sections of the same individuals’ EEG that were artifact-laden, we ran ICA and extracted artifact independent components (identified by an expert and labeled artifact by both ICLabel and MARA automated algorithms). We inserted the artifact ICs into that individual’s clean 30-second data segment to create an additional 16 artifact-added files. We then selected a channel from a simulated VEP dataset (included here as simulated_full.set) with a stereotyped and prominent simulated VEP waveform, in this case Oz, and added its timeseries (included here as simulated_singleChan.set) to each channel of the clean and artifact-added files to create two VEP datasets with a known ERP morphology (sim-artifact_a-p and sim-clean_a-p). For additional information about the creation of this simulated data and VEP data with a known ERP morphology, please refer to Monachino et al., in revision; DOI: https://doi.org/10.1101/2021.07.02.450946.</p> <p>Additional files included below are the HAPPE+ER data and pipeline quality metric output spreadsheets for the five GABA study data files for an example run, the output spreadsheet containing the ERP timeseries from the generateERPs script, the .mat file containing the parameter settings for HAPPE+ER for that run, an Excel file with the bad channels for each file, and a tutorial document illustrating the results of this example run. </p>
A generalized machine learning framework to predict the space-time yield of methanol from thermocatalytic CO2 hydrogenation
<p>Thermocatalytic CO<sub>2</sub> hydrogenation to methanol is an attractive decarbonization technology to combat climate change while producing a valuable platform chemical and energy carrier. However, predicting the performance of catalytic systems for this process remains a challenge. Herein, we present a machine learning framework to predict catalyst performance from experimental descriptors. A database of Cu-, Pd-, In<sub>2</sub>O<sub>3</sub>-, and ZnO-ZrO<sub>2</sub>-based catalysts with 1425 datapoints is compiled from literature and subjected to data mining. Accurate ensemble-tree models (<em>R</em><sup>2</sup> > 0.85) are developed to predict the methanol space-time yield (<em>STY</em>) from 12 descriptors, where the significance of space velocity, pressure, and metal content is revealed. The model prediction and its insights are experimentally validated, with a root mean squared error of 0.11 g<sub>MeOH</sub> h<sup>−1</sup> g<sub>cat</sub><sup>−1 </sup>between the actual and predicted methanol<em> STY</em>. The framework is purely data-driven, interpretable, cross-deployable to other catalytic processes, and serves as an invaluable tool for guided experiments and optimization.</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.