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1,478 results for “faces”
Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networks.
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Emotion Category and Face Perception Task Optimized for Multivariate Pattern Analysis
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Shared neural codes for visual and semantic information about familiar faces in a common representational space
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Market Power / Import demand elasticity faced by an exporter at 6-digit HS level from Solleder (2020)
<p><strong>Description</strong></p> <p>This dataset contains the market power of exporters at the country level for more than 4000 6-digit HS codes (HS 1992 / H0) from Solleder (2020). Market power is proxied by the inverse of the import demand elasticity faced by the exporting country. Elasticities are estimated following the method developed by Kee et al. (2008). For more information, please refer to Solleder (2020).</p> <p>The <em>dta </em>file can be opened with STATA 14 or above. The <em>csv</em> file is a comma-separated value file. The separator is ',', and the first row is variable names. The content is the same in both files. Variables are:</p> <ul> <li><em>exporter</em>: ISO 3166 3-character country codes, string; </li> <li><em>commoditycode</em>: product 6-digit HS codes in HS revision 1992 (H0), string;</li> <li><em>epsilon</em>: import demand elasticity faced by the exporter, numeric;</li> <li><em>epsilon_se</em>: standard error of <em>epsilon</em>, numeric;</li> <li><em>marketpower</em>: market power, inverse of the absolute value of the import demand elasticity faced by the exporter, numeric.</li> </ul> <p> </p> <p><strong>Reference</strong></p> <div> <div>Kee H.L., A. Nicita, M. Olarreaga 2008 'Import demand elasticities and trade distortions' Rev. Econ. Stat., 90 (4), pp. 666-682</div> <div> </div> <div>Solleder J.M. 2020 'Market power and export taxes' European Economic Review, Volume 125, 103425, ISSN 0014-2921, <a href="https://doi.org/10.1016/j.euroecorev.2020.103425">https://doi.org/10.1016/j.euroecorev.2020.103425</a>.</div> </div> <p> </p>
Long-term Memory (LTM) for famous Faces, Places, and common Objects
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Dataset for the publication entitled "An exact system of generation for face-milled hypoid gears with uniform depth taper: application to hypoid gear drives with high gear ratio"
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BIRAFFE2: The 2nd Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems
<p>This is our 2nd Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems (<strong>BIRAFFE2</strong>). It is a dataset consisting of <em><strong>electrocardiogram (ECG)</strong></em>, <em><strong>galvanic skin response (GSR)</strong></em>, changes in <em><strong>facial expression</strong></em> signals and <em><strong>hand movements</strong></em> (represented by gamepad's accelerometer and gyroscope) recorded during affect elicitation by means of <em><strong>audio-visual stimuli</strong></em> (from IADS and IAPS databases) and our proof-of-concept three-level <em><strong>emotion evoking game</strong></em>. All the signals were captured using portable and low-cost equipment: BITalino (r)evolution kit for ECG and GSR and Creative Live! web camera for face photos (further analyzed by MS Face API).</p> <p>Besides the signals, the dataset consists also of <em><strong>participants' self-assessment</strong></em> of their affective state after each stimuli (in the <em><strong>valence and arousal dimensions</strong></em>), <em><strong>"Big Five" personality traits</strong></em> assessment (using NEO-FFI inventory), and <em><strong>game involvement</strong></em>-related metrics (using GEQ questionnaire).</p> <p>In 1.1.0 version, RAW questionnaire data was included. The licence was changed from CC BY-NC-ND 4.0 to CC BY 4.0.</p> <p>For detailed description see <a href="https://doi.org/10.1038/s41597-022-01402-6">BIRAFFE2 Data Descriptor in Nature Scientific Data</a>.<br> For preview of the files before downloading the whole dataset see <em>sample-SUB211-[...]</em> files.</p> <p>All documents and papers that report on research that uses the BIRAFFE dataset should acknowledge this by <strong>citing the paper</strong>:<br> Kutt, K., Drążyk, D., Żuchowska, L., Szelążek, M., Bobek, S., & Nalepa, G. J. (2022). <strong>BIRAFFE2, a multimodal dataset for emotion-based personalization in rich affective game environments</strong>. <em>Scientific Data</em>, <em>9</em>, 274. <a href="https://doi.org/10.1038/s41597-022-01402-6">https://doi.org/10.1038/s41597-022-01402-6</a></p>
Dataset for the publication "Implementation of an exact completing method of generation for face-milled spiral bevel gears with uniform depth taper"
<p>This dataset contains geometric and graphics data associated with the referenced paper, enabling the reproduction of the conducted research. </p>
OpenForensics: Multi-Face Forgery Detection And Segmentation In-The-Wild Dataset [V.1.0.0]
<p>OpenForensics is the first large-scale dataset posing a high level of challenges. This dataset is designed with face-wise rich annotations explicitly for face forgery detection and segmentation. With its rich annotations, OpenForensics dataset has great potentials for research in both deepfake prevention and general human face detection. Project Page: https://sites.google.com/view/ltnghia/research/openforensics</p>
Documentary sources of case studies on the issues a data protection officer faces on a daily basis
<p>The dataset contains the text of the documents that are sources of evidence used in [1] and [2] to distill our reference scenarios according to the methodology suggested by Yin in [3].</p> <p>The dataset is composed of 95 unique document texts spanning the period 2005-2022. This dataset makes available a corpus of documentary sources useful for outlining case studies related to scenarios in which the DPO finds himself operating in the performance of his daily activities.</p> <p>The language used in the corpus is mainly Italian, but some documents are in English and French. For the reader's benefit, we provide an English translation of the title of each document.</p> <p>The documentary sources are of many types (for example, court decisions, supervisory authorities' decisions, job advertisements, and newspaper articles), provided by different bodies (such as supervisor authorities, data controllers, European Union institutions, private companies, courts, public authorities, research organizations, newspapers, and public administrations), and redacted from distinct professional roles (for example, data protection officers, general managers, university rectors, collegiate bodies, judges, and journalists).</p> <p>The documentary sources were collected from 31 different bodies. Most of the documents in the corpus (a total of 83 documents) have been transformed into Rich Text Format (RTF), while the other documents (a total of 12) are in PDF format. All the documents have been manually read and verified.<br> The dataset is helpful as a starting point for a case studies analysis on the daily issues a data protection officer face. Details on the methodology can be found in the accompanying papers.</p> <p>The available files are as follows:</p> <ul> <li><strong>documents-texts.zip</strong> --> contain a directory of .rtf files (in some cases .pdf files) with the text of documents used as sources for the case studies. Each file has been renamed with its SHA1 hash so that it can be easily recognized.</li> <li><strong>documents-metadata.csv</strong> --> Contains a CSV file with the metadata for each document used as a source for the case studies.</li> </ul> <p>This dataset is the original one used in the publication [1] and the preprint containing the additional material [2].</p> <p>[1] F. Ciclosi and F. Massacci, "The Data Protection Officer: A Ubiquitous Role That No One Really Knows" in IEEE Security & Privacy, vol. 21, no. 01, pp. 66-77, 2023, doi: 10.1109/MSEC.2022.3222115, url: https://doi.ieeecomputersociety.org/10.1109/MSEC.2022.3222115.</p> <p>[2] F. Ciclosi and F. Massacci, "The Data Protection Officer, an ubiquitous role nobody really knows." arXiv preprint arXiv:2212.07712, 2022.</p> <p>[3] R. K. Yin, Case study research and applications. Sage, 2018.</p>
Effect of the aspen leaf miner feeding damage on aspen leaf gas exchange and water relations from south-facing site on the University of Alaska Fairbanks campus: Fairbanks, Alaska 2018
This dataset addresses the effects of epidermal leaf mining by the aspen leaf miner (Phyllocnistis populiella) on the physiology and water relations of aspen leaves. The dataset contains measurements of gas exchange, water potential, water content, and delta13C of aspen leaves manipulated to bear leaf mining damage on the top (adaxial) leaf surface only, the bottom (abaxial) leaf surface only, or no mining damage.
RFP01 Properties of large hillslope blocks and cliff faces along the cottonwood limestone near the konza prairie nature trail
This data is a collection of point observations and measurments of large rock fragments on grassland hillslopes. Data was collected from 30 hillslope transects that extend downslope perpidicular from the bedrock cliff formed from the Cottonwodd limestone. Transects are 30 meters long and 1 meter wide. Observations of blocks include properites such as size, shape, and surface weathering. This data set also includes observtions of cliff properties associated with each transpect location. Measurments we made in field by hand for rock fragments larger than pebble (>64mm).
Investigating the Effects of Embodiment on Emotional Categorization of Faces and Words in Children and Adults
<p>The three data files uploaded here contain the data used for the analyses in experiments 1a, 1b, and 2 as described in the article carrying the same title as this dataset, published in the journal Frontiers in Psychology. All analyses were carried out in SPSS version 22 as described in the published article.</p> <p>Article Abstract:</p> <p>The facial feedback hypothesis (FFH) indicates that besides being involved in the production of facial expressions, the musculature of the face also influences one’s perception of emotional stimuli. Recently, this effect has been the focus of increased scrutiny as efforts to replicate a key study with adult participants supporting this hypothesis, using the so-called “pen-in-the-mouth” task, have not been successful at several labs. Our series of experiments attempted to investigate whether the assumed embodiment effect can be reproduced in a simplified emotional categorization task for emotional faces and words. We also wanted to test whether the embodiment effect can be detected in children because it is assumed that their bodily processes are especially closely linked with their sensory and cognitive processes. Our experiments involved child and adult participants categorizing faces and words as positive or negative as quickly as possible, while inducing a positive or negative facial or bodily state (holding a straw in the mouth such that a smile or a frown was generated, or creating a positive or negative body posture). The positive or negative facial and bodily states could therefore be either congruent or incongruent with the valence of the target face and word stimuli. Our results did not show any significant differences between the congruent and incongruent conditions in either children or adults. This suggests that embodiment effects either do not significantly impact valence-based categorization or are not strong enough to be detected by our approach considering the sample size in the present study.</p>
Bihār. Seated Buddha, rear face with stūpa and ye dharmā formula.
<p>Bihār (site not recorded). Seated Buddha on a lotus pedestal against a decorated throne-back surmounted by a halo, giving his First Sermon; the wheel and deer below him flanked by monks; on the back an engraved <em>stūpa</em> with <em>ye dharmā</em> formula, <em>circa</em> eighth century (Height: 33 centimetres). Purchased of <a href="https://research.britishmuseum.org/system_pages/beta_collection_introduction/beta_collection_search_results.aspx?people=200978&peoA=200978-3-17">Robert Montgomery Martin </a>by the British Museum and registered as 1854, 0214.1.</p>
Representative Structures from Molecular Dynamics Simulations of the Inward Facing and Outward Facing States of LaINDY
<p>This upload is a supplementary data set for the following publication: <a href="https://doi.org/10.7554/eLife.61350">D. B. Sauer, N. Trebesch, J. J. Marden, N. Cocco, J. Song, A. Koide, S. Koide, E. Tajkhorshid, and D.-N. Wang. "Structural basis for the reaction cycle of DASS dicarboxylate transporters." <em>eLife</em>. <strong>9</strong>, e61350. DOI: 10.7554/eLife.61350</a>. Please see the main publication for the methods, analysis, and discussion associated with this data set.</p>
Resilient farm demographics withstand, adapt, or transform in the face of competitive pressure, technological change, and the expected lifestyles of future generations
<p>Farm demographics has been recognized as an important driver of structural change in European agriculture. Focus groups and computer simulations on farm demographic change were used to better understand its role for the case study regions of the Altmark in the eastern part of Germany and Flanders in the northern part of Belgium. According to these analyses, many potential agricultural entrants are deterred by what they view as a poor quality of life that farming offers. This applies to farm successors as well as hired workers. For higher attractiveness of agriculture, policy objectives should address the social image of farming as well as revitalize rural areas. Increasingly critical is the demand for skilled hired labour. However, policies dealing with farm demographic change ignore these needs and focus almost exclusively on farm succession. Particularly, the direct payment system, including additional support for small farms and young farmers, must be re-evaluated for its effectiveness. The analyses provide evidence that this system constrains European agricultural development more than assists it; ultimately preventing farms from adapting and transforming.</p>
Datasets for "Stable nanofacets in [111] tilt grain boundaries of face-centered cubic metals"
<p>This repository contains raw data for the paper “Stable nanofacets in [111] tilt grain boundaries of face-centered cubic metals”. It contains the input files, scripts, and raw data of the simulations, as well as raw data from scanning transmission electron microscopy.</p>
Public opinion poll "War, Peace, Victory and the Future" – National face-to-face opinion poll representative of the population in government-controlled territories of Ukraine on the war-related issues (June 2023)
The face-to-face survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation in cooperation with the Centre for Political Sociology from 5 to 15 June 2023. A total of 2,001 respondents aged 18 or older took part in the survey in Vinnytsia, Volyn, Dnipropetrovsk, Zhytomyr, Zakarpattia, Zaporizhzhia, Ivano-Frankivsk, Kyiv, Kirovohrad, Lviv, Mykolaiiv, Odesa, Poltava, Rivne, Sumy, Ternopil, Kharkiv, Kherson, Khmelnytskyi, Cherkasy, Chernihiv, and Chernivtsi regions, and the city of Kyiv (in Zaporizhzhia, Kharkiv, and Kherson regions – only in the territories controlled by Ukraine and not affected by hostilities). The sampling technique used in the survey is multi-stage, with a random selection of localities in the first stage and a quota-based selection of respondents in the final stage. The random selection is representative of the demographic structure of the adult population in the areas covered by the survey at the beginning of 2022. The maximum sampling error shall not exceed 2.3%. At the same time, it is necessary to take into account systematic deviations in the sample caused by the forced migration of millions of citizens due to the Russian-Ukrainian war. COMPOSITION OF MACRO-REGIONS: West – Volyn, Zakarpattia, Ivano-Frankivsk, Lviv, Rivne, Ternopil, and Chernivtsi regions; Center – Vinnytsia, Zhytomyr, Kyiv, Kirovohrad, Poltava, Sumy, Khmelnytskyi, Cherkasy, and Chernihiv regions, and the city of Kyiv; South – Zaporizhzhia, Mykolaiiv, Kherson, and Odesa regions; East – Dnipropetrovsk and Kharkiv regions. This dataset contains the original survey data. The SPSS file (.sav) is the original file. It has been exported to an Excel file. The content of the corresponding XLSX file should be identical to the original SAV file. The SAV file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire and an English translation have also been included in this data collection as separate PDF files. In addition, the dataset includes a file of "selected findings", which documents some of the key findings of the survey in the form of analytical summaries and descriptive statistics. The report was prepared by the civil society organisation OPORA.
Water risks to hydropower projects in the face of climate change
<p>This repository hosts the main outputs from an analysis using the <a href="https://waterriskfilter.org/">WWF Water Risk Filter</a> to demonstrate how one such tool can be used to screen for a variety of risks at a global scale, including risks to riverine ecosystems from both climate change and hydropower as well as risks to hydropower projects — and operators, owners, and investors — from climate change and potential regulatory or reputational risk arising from negative impacts to ecosystems. The study <a href="https://www.mdpi.com/2073-4441/14/5/721">Using the WWF Water Risk Filter to Screen Existing and Projected Hydropower Projects for Climate and Biodiversity Risks (DOI 10.3390/w14050721) </a>was published in the special issue of the MDPI journal Water: <a href="https://www.mdpi.com/journal/water/special_issues/hydrometeorological_hazards">"Hydro-Meteorological Hazards under Climate Change"</a>.</p> <p>This product incorporates data from the GRanD v1.3 database which is © Global Water System Project (2011), and from the FHReD database beta version, both datasets available at <a href="http://globaldamwatch.org/">globaldamwatch.org</a> . The source code used in this study is available at <a href="https://github.com/rafaexx/hydropowerClimateChange">https://github.com/rafaexx/hydropowerClimateChange</a></p> <p>See the interactive maps using this data at <a href="https://rcamargo.shinyapps.io/HydropowerClimateChange">https://rcamargo.shinyapps.io/HydropowerClimateChange</a></p>
Infant N290 event-related potentials and stimulus-specific adaptation to face stimuli
<p>Data for publication: "Neural specialization to human faces at the age of 7 months".</p> <p>Further details will be available at: https://github.com/yrttiahoS/ssa/</p> <p> </p> <p>Metadata:</p> <p>I. Event-related potentials</p> <p>The current study investigated both cortical sensitivity and categorical specificity through event-related potentials (ERPs) previously implicated in face processing in 7-month-old infants (N290) and adults (N170). Using a category-specific repetition/adaptation paradigm, cortical specificity to human faces, or control stimuli (cat faces), was operationalized as changes in ERP amplitude between conditions where a face probe was alternated with categorically similar or dissimilar adaptors. In adults, increased N170 for human vs. cat faces and category-specific release from adaptation for face probes alternated with cat adaptors was found. In infants, a larger N290 was found for cat vs. human probes. Category-specific repetition effects were also found in infant N290 and the P1-N290 peak-to-peak response where latter indicated category-specific release from adaptation for human face probes resembling that found in adults.</p> <p>*Dataset files: N290_P1_infant, N170_P1_adult.sav</p> <p> </p> <p>*EEG data in EEGLAB’s format,</p> <p>Filenames indicate type of data with:</p> <p>1) Initial numerical code indicating (anonymized) participant number, "adult/infant" indicating participant group</p> <p>2) f1/c2/f3/c3 indicating stimulus conditions probe:face, adaptor:face / probe:cat, adaptor:cat / probe:face, adaptor:cat / probe:cat,adaptor:face, respectively</p> <p> 3) Final number _1_ or _2_ indicates block number for adult participants (in the order of presentation)</p> <p>Files with only codes 1) and 2) are continuous data with all triggers included</p> <p> </p> <p>*video-based Quality Control is listed in file: anon_vQC2022-09-22.xlsx*video-based Quality Control is listed in file: anon_vQC2022-09-22.xlsx</p> <p> </p> <p>*Analysis syntax: https://github.com/yrttiahoS/ssa/</p> <p> </p> <p>*Dataset updates: TBA at https://github.com/yrttiahoS/ssa/</p> <p> </p> <p> </p> <p>II. Temperament questionnaire</p> <p>*Data collection: Temperament data were collected from participants of an infant event-related potential (ERP) study as potential correlate of outcome variables and as a descriptor of the participant group. Participants were 7-month-old infants from families volunteering in the brain research study, which were contacted through information from a population registry sample. Infant temperament was assessed by parent-reported IBQ-R short form (Putnam, S. P., Helbig, A., Gartstein, M.A., Rothbart, M.K. & Leerkes, E. M. (2014). <em>Development and assessment of short and very short forms of the Infant Behavior Questionnaire-Revised.</em> Journal of Personality Assessment, 96, 445-458. Finnish translation: Professor Katri Räikkönen-Talvitie and the Developmental Psychology Research Group University of Helsinki, Finland)</p> <p>*Authors of the dataset: Santeri Yrttiaho, Mikko Peltola, Anneli Kylliäinen, Tiina, Parviainen, Jari Hietanen</p> <p>*Site of data collection: Human Information Processing laboratory, Tampere University, Finland</p> <p>*Funding: Emil Aaltonen Foundation, Tampere University, Academy of Finland</p> <p>*Participant demographics: Participant group is described by age of M(SD) = 30.5(0.5) weeks. Participants were from Tampere metropolitan area, Finland. Data were collected during Fall 2020 (October 16th – December 7th, 2020).</p> <p>*Data content. Initial data will contain group level statistics and the individual data will be made available as additional files after publication of study results in a peer-reviewed article. Data is anonymized. Individual data contains IBQ-R short form items, scales, and factors as well as participant gender and age in weeks. Data also includes variable indicating whether the participant was included in the final ERP analysis after EEG quality control.</p> <p>*variable names: see 'Scale abbreviations.docx' and for full discussion the original article by Putnam et al. (2014).</p> <p>*Dataset files</p> <p>1. IBQ-R-short-Finnish_dataset.sav / data of items, scales, and factors</p> <p>2. SSA_infant_ibq-r_summary.sps / syntax for computing scores</p> <p>3. IBQ-R-short-Finnish_descriptives.spv / output of descriptive statistics</p> <p>4. Scale abbreviations.docx / explanation of variable names</p> <p> </p> <p> </p> <p> </p> <p> </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.