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28,427 results for “group”
3D Archaeological Greek Pottery: MHNC-UP-020053, Attic black-figure shoulder lekythos, Group of Vatican G 52
<p><strong>This 3D dataset is related to the publication</strong>:</p> <ul> <li>Moitinho de Almeida, V. (2023). "<a href="https://www.researchgate.net/publication/353038967_Contributions_of_3D_digital_methods_and_techniques_to_the_study_of_ancient_pottery">Contributions of 3D digital methods and techniques to the study of ancient pottery</a>". In <em>Myths, Gods, and Heroes. Greek vase collections in Portugal / Mitos, Deuses e Heróis. As coleções de vasos gregos em Portugal</em>. R. Morais, R. Centeno, D. Ferreira (eds.). Câmara Municipal de Santa Maria da Feira - Museu Convento dos Lóios; Reitoria da Universidade do Porto; Faculdade de Letras da Universidade do Porto; Imprensa da Universidade de Coimbra. Pp.269-291. (ISBN: 978-989-8183-25-5)</li> </ul> <p>3D processed dataset for object MHNC-UP-020053 in the Museu de História Natural e da Ciência da Universidade do Porto (MHNC-UP), Portugal. <strong>CC BY-NC-SA 4.0 license</strong>.</p> <p>MHNC-UP-020053 is an Attic black-figure shoulder <em>lekythos</em>, Group of Vatican G 52, dating from c. 550-500 BCE and unknown provenance (Rocha Pereira & Morais, 2007; Morais, 2019; Morais et al., 2021).</p> <p><strong>Aims</strong>: 3D digital documentation; morphological characterization; technological and functional analysis of archaeological Greek pottery.</p> <p><strong>Data acquisition</strong>: at the MHNC-UP, with a portable non-contact structured white light scanner, Breukmann smartSCAN3D-HE, equipped with stereo colour cameras at 250 mm FOV. Additional metadata is included in the associated spreadsheet.<br><strong>Data processing</strong>: 35 scans aligned and merged. MHNC-UP-020053_3D01.ply: non-manifold edges, self-intersections, small components, and small tunnels in the mesh automatically fixed, noise data removed; orientation and position normalised. MHNC-UP-020053_3D01-holesFilled.ply: holes filled for calculation of material density, filling volume, and centre of mass. Mesh is not watertight (inner surface not digitised due to occlusion). Additional metadata is included in the associated spreadsheet.</p> <p>Access to the MHNC-UP-020053 was granted by the MHNC-UP.</p> <p>When citing this material: please include the original inventory ID (MHNC-UP-020053) reference to the physical object.</p>
The metal content of the hot atmospheres of galaxy groups - supporting data
<p>Supporting data used to generate the figures included in the review chapter "The metal content of the hot atmospheres of galaxy groups", to appear in the MDPI journal "Universe". These values were collected and compiled from existing literature; each text file lists the relevant references to the original articles where various sets of results were initially published. </p>
The Quest for the Missing Dust: New Herschel Maps of Local Group Galaixes (LMC, SMC, M31, M33) that Restore Previously-Missed Extended Emission, Along With SED-Fitting Results, Hydrogen Gas Maps, and Swift UV Observations
<p>Here we provide the data products from publications:</p> <p>Clark, C.J.R., et al., <em>The Quest for the Missing Dust: I – Restoring Large Scale Emission in Herschel Maps of Local Group Galaxies</em>, ApJ 921 35</p> <p>Clark, C.J.R., et al., <em>The Quest for the Missing Dust: II – Two Orders of Magnitude of Evolution in the Dust-to-Gas Ratio Resolved Within Local Group Galaxies</em>, ApJ 946 42</p> <p>This data concerns four Local Group galaxies: the Large Magellanic Cloud (LMC), the Small Magellanic Cloud (SMC), M31, and M33.</p> <p> </p> <p>For each galaxy, we provide our new Herschel maps, as described in the above publications, which were combined in Fourier space ('feathered') with Planck, IRAS, and COBE data, in order to restore extended emission that was removed from previous Herschel reductions for these galaxies.</p> <p>For each galaxy, we provide this new Herschel data for 5 Hershcel bands: the PACS 100 and 160 <span>\(\mu\)</span>m bands, and the SPIRE 250, 350, and 500 <span>\(\mu\)</span>m bands. This data is provided in FITS format, with one FITS file for each band for each galaxy. Each of these files contains 4 extensions. Extension 1 (IMAGE) provides the standard feathered map. Extension 2 (UNC) provides the uncertainty map. Extension 3 (MASK) provides a binary mask map indicating the portion of the data where reliable, fully-feathered high-resolution coverage is available. Extension 4 provides the foreground-subtracted version of the feathered map (FGND_SUB), the header of which also describes the uncertainty on that subtraction. All maps are in units of MJy/sr (except the MASK extension, which is boolean).</p> <p> </p> <p>We also provide the outputs of our Spectral Energy Distribution (SED) fitting to this data, as described in the publications. For each galaxy, we provide FITS files giving the median value of each parameter in each pixel, and maps of the uncertainties on those medians (being the 68.3% quantile around the median). The parameters are dust mass surface density (SED_Sigma_Mass.fits), dust temperature (SED_Temp.fits), beta 1 (SED_Beta1.fits), beta 2 (SED_Beta2.fits), break wavelength (SED_Break.fits), and 500 <span>\(\mu\)</span>m excess (SED_Excess500.fits). Each of these files contain 2 extensions. Extension 1 (median) provides the map of pixel parameter median values. Extension 2 (uncert) provides the map of uncertainties on those medians.</p> <p>Additionally, we provide the full posterior probability distribution for all SED parameters, consisting of 1000 posterior samples, for all pixels, in the form of a FITS file containing a 4-dimensional hypercube, with axes corresponding to right ascension, declination, parameters (in order: dust mass surface density, dust temperature, beta 1, beta 2, break wavelength, and 500 <span>\(\mu\)</span>m excess), and samples. This is provided as a gzip compressed FITS file for each galaxy.</p> <p>Furthermore, provide the Swift-UVOT maps used in Paper II. This data is provided for Swift-UVOT bands W1, W2, and M2. For each band, we provide a FITS file containing 3 extensions. Extension 1 (SURF_BRI) provides the map of surface brightness in MJy/sr (converted using the Swift-UVOT zero points given in Breeveld et al., 2011). Extension 2 (RATE) provides the map of count rate (in photons/sec). Extension 3 (EXP) provides the map of exposure time (in sec). The maps for the LMC and SMC are those presented in Hagen et al. (2017). The maps for M31 and M33 are were reduced following the same process as those in Hagen et al. (2017), and will be fully presented in Decleir et al. (in prep.), but are provided here for the purposes of reproducibility.</p> <p>Lastly, for each galaxy, we provide our maps of the hydrogen surface density (Sigma_H.fits), and dust-to-gas ratio (DtG.fits). None of the maps presented have had deprojection corrections applied</p> <p> </p>
Golden Agents - Amsterdam Corporate Group Portraits
<p>The Amsterdam Corporate Group Portraits dataset contains biographical information on persons depicted on institutional/corporate group portraits in the seventeenth and eighteenth century in Amsterdam. The dataset is part of the <a href="https://goldenagents.org/">Golden Agents</a> project.</p> <p>The original data were collected by Norbert Middelkoop and published as attachment to his dissertation <a href="https://hdl.handle.net/11245.1/509fbcc0-8dc0-44ae-869d-2620f905092e">Schutters, gildebroeders, regenten en regentessen</a> (2019). The Golden Agent project took his data and heavily structured these, so that they could be used in the project's infrastructure infrastructure.</p> <p>We kept the original structure of the dataset, so that we separate information on:</p> <ul> <li>Corporate group portraits (visual works)</li> <li>Poorters [=Burghers] (persons)</li> <li>Regentessen [=Regents (F)] (persons)</li> <li>Regenten [=Regents (M)] (persons)</li> <li>Gildenleden [=Guild members] (persons)</li> </ul> <p>For all the persons, if available, we modelled information on their name, the portrait they are depicted on, when they were a regent or churchmaster, when they were a member of the civic guards (schutterij), when they were a member of a guild, and when they were a member of the city council. Besides this, we added information on their birth and death date, and their marriages.</p> <p>Through linksets (see the <a href="https://github.com/knaw-huc/golden-agents-amsterdam-corporate-group-portraits/blob/master/linksets/README.md">linkset documentation</a>) we linked the persons to Wikidata and to among others the <a href="https://archief.amsterdam/">Amsterdam City Archives</a>. These linksets also serve as a way to link the persons to other datasets in the Golden Agents project.</p> <p>This release marks the stable version of this data at the end of the Golden Agents project (2016-2022).</p>
20 news group (20ng)
<p>20 Newsgroups (20NG) is a classical and popular dataset for experiments in text applications of machine learning techniques. It contains 18,846 newsgroups documents, partitioned almost evenly across 20 different newsgroups categories. </p> <p>http://qwone.com/~jason/20Newsgroups/</p> <p><strong>The files:</strong><br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_<k>.pkl: pandas DataFrame with k-cross validation partition.</p> <p>The .zip contains all aforementioned files + the tfidf representation in the CSR matrix format.</p> <p><strong>Label Definition: (Score File)</strong></p> <p>0 atheist resources<br> 1 computer graphics<br> 2 computer os ms windows misc<br> 3 computer system ibm pc hardware<br> 4 computer system mac hardware<br> 5 computer windows x<br> 6 misc miscellaneous for sale<br> 7 rec autos<br> 8 rec motorcycles<br> 9 rec sport baseball<br> 10 rec sport hockey<br> 11 science crypt<br> 12 science electronics<br> 13 science med<br> 14 science space<br> 15 society religion christian<br> 16 talk politics guns<br> 17 talk politics mideast<br> 18 talk politics misc miscellaneous<br> 19 talk religion misc miscellaneous</p>
SoundDesc: Cleaned and Group-Filtered Splits
<p>This upload contains dataset splits of <em>SoundDesc</em> [1] and other supporting material for our paper:</p> <p><strong>Data leakage in cross-modal retrieval training: A case study </strong><a href="https://arxiv.org/abs/2302.12258">[arXiv]</a> <a href="https://doi.org/10.1109/ICASSP49357.2023.10094617">[ieeexplore]</a></p> <p>In our paper, we demonstrated that a data leakage problem in the previously published splits of SoundDesc leads to overly optimistic retrieval results.<br> Using an off-the-shelf audio fingerprinting software, we identified that the data leakage stems from duplicates in the dataset.<br> We define two new splits for the dataset: a cleaned split to remove the leakage and a group-filtered to avoid other kinds of weak contamination of the test data.</p> <p>SoundDesc is a dataset which was automatically sourced from the BBC Sound Effects web page [2]. The results from our paper can be reproduced using <em>clean_split01</em> and <em>group_filtered_split01</em>.</p> <p><em>If you use the splits, please cite our work:</em></p> <p>Benno Weck, Xavier Serra, "Data Leakage in Cross-Modal Retrieval Training: A Case Study," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10094617.</p> <pre><code>@INPROCEEDINGS{10094617, author={Weck, Benno and Serra, Xavier}, booktitle={ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, title={Data Leakage in Cross-Modal Retrieval Training: A Case Study}, year={2023}, volume={}, number={}, pages={1-5}, doi={10.1109/ICASSP49357.2023.10094617}} </code></pre> <p>References:</p> <p>[1] A. S. Koepke, A. -M. Oncescu, J. Henriques, Z. Akata and S. Albanie, "Audio Retrieval with Natural Language Queries: A Benchmark Study," in IEEE Transactions on Multimedia, doi: 10.1109/TMM.2022.3149712.</p> <p>[2] https://sound-effects.bbcrewind.co.uk/</p>
Radiographic outcomes for Biodentine group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months
<p>Radiographic outcomes for Biodentine group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months</p>
Clinical outcome for TheraCal LC group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months
<p>Clinical outcome for TheraCal LC group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months</p>
Clinical outcome for MTA group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months
<p>Clinical outcome for MTA group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months</p>
Radiographic outcomes for MTA group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months
<p>Radiographic outcomes for MTA group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months</p>
Clinical outcome for Biodentine group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months
<p>Clinical outcome for Biodentine group for postoperative pain, tenderness, and neural sensibility at the patient recall period of 21 days, 3 months, and 12 months</p>
Data of Chinese treatment group for the research work "Disentangling material, social, and cognitive determinants of human behavior and belief".
<p>This repository contains data files of Chinese treatment group for the research work "Disentangling material, social, and cognitive determinants of human behavior and belief".</p>
FEDORA. Excerpts from essays, transcript of interviews and group discussions on students' future perception. Part 3: Interviews, Finland.
<p>Finnish-language dataset. Related to a research article that is awaiting acceptance for publication: <em>Future, technology and agency: Students’ experiences from a course on futures thinking and quantum computing</em>.</p> <p>As per ethical concerns and participants' consent, the dataset is given in a fully anonymised form. Instead of students' interviews (the context of which is given in the article). In a nutshell, 21 upper-secondary school students were interviewed in 2018 regarding their experiences on taking an experimental science course that combined ideas from futures thinking and quantum computing. The present dataset contains all 245 transcribed passages from 21 student interviews that were initially marked as relevant to the research goals (i.e. how students saw their conceptions change over the course). Additionally, for each passage the final coding that was used in the analysis for the research paper is shown. The "number-letter codes" were used as shorthands; the full names of the codes correspond closely with the final, English-language codes in the paper.</p> <p>To preserve full anonymity, students are not identified by any marker or pseudonym here; rather, the passages are given alphabetically. The start and end of passages has not been checked for additional or missing first and last characters. Please also note that the character > marks change of speaker. Identifying the interviewer and interviewee should be straighforward based on the context.</p> <p>Please contact the corresponding author for more information.</p> <p> </p> <p>--</p> <p> </p> <p><a href="https://zenodo.org/communities/futuresthinking?page=1&size=20">FEDORA Project</a> README:</p> <p> </p> <p><strong>README</strong></p> <p><strong>Data Set Title:</strong> “FEDORA. Excerpts from essays, transcript of interviews and group discussions on students’ future perception. Finland"</p> <p><strong>Data Set Author/s:</strong> Antti Laherto, Tapio Rasa, Elina Palmgren (University of Helsinki)</p> <p><strong>Data Set Contact Person/s</strong>: Tapio Rasa<strong> </strong>(University of Helsinki), ORCID 0000-0003-1315-5207, tapio.rasa@helsinki.fi;</p> <p><strong>Data Set License</strong>: this data set is distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.</p> <p><strong>Publication Year</strong>: 2021</p> <p><strong>Project Info</strong>: FEDORA<strong> </strong>(Future-oriented Science EDucation to enhance Responsibility and engagement in the society of Acceleration and uncertainty<strong> , </strong>funded by European Union, Horizon 2020 Programme. Grant Agreement num.<strong> </strong>872841,<br> www.fedora-project.eu)</p> <p> </p> <p><strong>Data set Contents</strong></p> <p>The data set consists of:</p> <p>One spreadsheet file, provided in two alternative formats (CSV and XLSX).</p> <p>Future_technology_agency_DATA_CSV.csv</p> <p>Future_technology_agency_DATA_XLSX.xlsx</p> <p> </p> <p><strong>Data set Documentation</strong></p> <p><em>Given above this README, on the ZENODO repository. https://zenodo.org/record/4734161</em></p>
Block-group level mode choice parameters for New York City and New York State
<p>We provide two datasets of census block group-level mode choice parameters for New York City and New York State. The parameters are estimated by GLAM logit model using Replica's synthetic population datasets (For details of the GLAM logit model, please refer to <a href="https://github.com/BUILTNYU/GLAM-Logit">BUILTNYU/GLAM-Logit (github.com)</a>). Each row contains a set of mode choice parameters for each block-group OD pair and one of the four population segments (low-income, not low-income, students, and senior population). Six trip modes are considered: private auto, public transit (such as buses, light rail, and subways), on demand auto (taxi or TNC services such as Uber or Lyft), biking (including e-bike), walking, and carpool. Parameters of twelve mode attributes are estimated, including, auto travel time, transit in-vehicle time, transit access time, transit egress time, number of transit transfers, non-vehicle travel time, trip cost, and five alternative specific constants (setting carpool as the reference level).</p> <p>In New York City, the average value of time (VOT) of low-income population is 21.67$/hour, the average VOT of not low-income population is 28.05$/hour, the average VOT of student population is 10.96$/hour, and the average VOT of senior population is 10.93$/hour. In New York State, the average value of time (VOT) of low-income population is 9.63$/hour, the average VOT of not low-income population is 13.95$/hour, the average VOT of student population is 7.40$/hour, and the average VOT of senior population is 6.26$/hour. </p> <p>The empirical distribution of agent-level parameters is neither Gumbel nor Gaussian, which reveals a regional divergence of the value of time and mode preference, indicating potential inequity issues in the transportation system. This is infeasible for conventional discrete choice models (DCMs) to capture. </p>
Block-group level predicted mode share for New York City and New York State
<p>We provide two datasets of predicted mode share, one for New York City and another for New York State. Each row contains the mode proportion of trips along a census block group-level OD pair made by one of the four population segments: low-income, not low-income, students, and senior population. Six trip modes are considered: private auto, public transit (such as buses, light rail, and subways), on demand auto (taxi or TNC services such as Uber or Lyft), biking (including e-bike), walking, and carpool.</p> <p>The prediction is based on GLAM logit model calibrated with Replica's statewide synthetic population dataset. The in-sample prediction accuracy is quite competitive, with an overall accuracy of 90.28% in New York State and 88.63% in New York City. For more details of the model, please refer to our Github repository: <a href="https://github.com/BUILTNYU/GLAM-Logit">BUILTNYU/GLAM-Logit (github.com)</a></p>
Difference and number of works published over the years grouped by the objective of the generative process
<p>Difference and number of works published over the years grouped by the objective of the generative process. Part of the study "What do we mean by GenAI?"</p>
Number of works grouped by AI technique employed
<p>Number of works grouped by AI technique employed. Part of the study "What do we mean by GenAI?"</p>
Number of works grouped by objective, domain and AI technique employed
<p>Number of works grouped by objective, domain and AI technique employed. Part of the study "What do we mean by GenAI?"</p>
Number of works grouped by domain of application
<p>Number of works grouped by domain of application. Part of the study "What do we mean by GenAI?"</p>
Number of works published over the years grouped by generated content type
<p>Number of works published over the years grouped by generated content type. Part of the study "What do we mean by GenAI?"</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.