Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
222
datasets available to search
ShareScore release 0.9.0
Dataset results
222 results for “surprise”
Behavioral, physiological, and neural signatures of surprise during naturalistic sports viewing
Open the record for dataset details and reuse information.
Orthophoto & DEM (MNE) issues d'images drone, UAV, Surprise, Mayotte - 20220912
"Ce jeu de données présente les résultats des traitements photogrammétriques d'images de drone DJI Mavic 2 Pro UAV acquises sur le site de Surprise, Mayotte à la date suivante : 20220912. <br> <br>Dans le cadre de la collaboration entre le Projet G2OI et Mayotte FMR (Future Maore Reef), l'objectif de la mission vise à étudier l'implantation de récifs artificiels dans le lagon de Mayotte sur plusieurs sites. En espace aérien contrôlé, les vols ont été exécutés en regard de la réglmentation et en contact avec la tour de contrôle de l'aéroport de Mayotte. <br> <br> <br><br>Le paramétrage du logiciel OpenDroneMap est partagé pour permettre la reproductibilité ou l'amélioration des traitements proposés:<br> [ <br> { <br> 'name': 'orthophoto-resolution', <br> 'value': 1 <br> }, <br> { <br> 'name': 'auto-boundary', <br> 'value': true <br> }, <br> { <br> 'name': 'dem-resolution', <br> 'value': '2.0' <br> }, <br> { <br> 'name': 'dsm', <br> 'value': true <br> } <br> ] <br> <br><b>Le dépôt est composé des éléments suivants:</b> <br> - 00_: Planche d'aperçu des images <br> - DCIM.zip: Images brutes issues du drone <br> - GPS.zip: Geopackage contenant l'emprise du survol ainsi que la géolocalisation des images accompagnées de leurs miniatures dans la table d'attribut en base64 <br> - METADATA.zip: Métadonnées au format ISO19115, Rapports avec miniatures des images de drone (dossier tb) et statistiques de vols. <br> - PROCESSED_DATA.zip: Orthophoto, DEM, nuages de points, ... <br> <br><b>Arborescence d'origine:</b> <br>└─ 20220912_MYT-surprise_UAV-02_2 <br> └─ DCIM <br> └─ GPS <br> └─ METADATA <br> └─ tb <br> └─ PROCESSED_DATA <br> <br><b>Informations de survol:</b> <br>Différentes hauteurs de vols, angles des transects et angles de caméra ont été utilisés dans le but d'obtenir des données de qualité en fonction des conditions météorologiques et marines. <br>- Camera model and parameters: <br> Make: Hasselblad <br> Model: L1D-20c <br> Width: 5472 <br> Height: 3648 <br> Focal: 28 <br> WhiteBalance: Manual <br> ExposureMode: Auto Exposure <br> ColoSpace: sRGB <br> EV: -0.7 <br> MeteringMode: CenterWeightedAverage <br> Camera Pitch: -75.00 <br> <br>- Survey informations: <br> No Images: 336 <br> Median height: 70 meters <br> Survey area: 6.49 hectares <br> Survey from: 2022:09:12 12:31:19 to: 2022:09:12 12:45:40 <br>"
Orthophoto & DEM (MNE) issues d'images drone, UAV, Surprise, Mayotte - 20220912
"Ce jeu de données présente les résultats des traitements photogrammétriques d'images de drone DJI Mavic 2 Pro UAV acquises sur le site de Surprise, Mayotte à la date suivante : 20220912. <br> <br>Dans le cadre de la collaboration entre le Projet G2OI et Mayotte FMR (Future Maore Reef), l'objectif de la mission vise à étudier l'implantation de récifs artificiels dans le lagon de Mayotte sur plusieurs sites. En espace aérien contrôlé, les vols ont été exécutés en regard de la réglmentation et en contact avec la tour de contrôle de l'aéroport de Mayotte. <br> <br> <br><br>Le paramétrage du logiciel OpenDroneMap est partagé pour permettre la reproductibilité ou l'amélioration des traitements proposés:<br> [ <br> { <br> 'name': 'orthophoto-resolution', <br> 'value': 1 <br> }, <br> { <br> 'name': 'auto-boundary', <br> 'value': true <br> }, <br> { <br> 'name': 'dem-resolution', <br> 'value': '2.0' <br> }, <br> { <br> 'name': 'dsm', <br> 'value': true <br> } <br> ] <br> <br><b>Le dépôt est composé des éléments suivants:</b> <br> - 00_: Planche d'aperçu des images <br> - DCIM.zip: Images brutes issues du drone <br> - GPS.zip: Geopackage contenant l'emprise du survol ainsi que la géolocalisation des images accompagnées de leurs miniatures dans la table d'attribut en base64 <br> - METADATA.zip: Métadonnées au format ISO19115, Rapports avec miniatures des images de drone (dossier tb) et statistiques de vols. <br> - PROCESSED_DATA.zip: Orthophoto, DEM, nuages de points, ... <br> <br><b>Arborescence d'origine:</b> <br>└─ 20220912_MYT-surprise_UAV-02_3 <br> └─ DCIM <br> └─ GPS <br> └─ METADATA <br> └─ tb <br> └─ PROCESSED_DATA <br> <br><b>Informations de survol:</b> <br>Différentes hauteurs de vols, angles des transects et angles de caméra ont été utilisés dans le but d'obtenir des données de qualité en fonction des conditions météorologiques et marines. <br>- Camera model and parameters: <br> Make: Hasselblad <br> Model: L1D-20c <br> Width: 5472 <br> Height: 3648 <br> Focal: 28 <br> WhiteBalance: Manual <br> ExposureMode: Auto Exposure <br> ColoSpace: sRGB <br> EV: -0.7 <br> MeteringMode: CenterWeightedAverage <br> Camera Pitch: -75.00 <br> <br>- Survey informations: <br> No Images: 385 <br> Median height: 70 meters <br> Survey area: 8.11 hectares <br> Survey from: 2022:09:12 14:29:06 to: 2022:09:12 14:43:35 <br>"
Dataset, Survey, and R Notebooks for "Does Surprisal Predict Code Comprehension Difficulty?"
<p>(Version 1.1 Update)</p> <p>- Removed anonymization after reviewing period ended and paper was accepted at Cogsci 2020.</p> <p>- Added Qualtrics Survey in exported form</p> <p>Dataset and R analysis scripts for the paper "Does Surprisal Predict Code Comprehension Difficulty?". For more details, see "ComprehensionREADME.md" in the included zip file.</p>
Image database to supplement "Paulus, F.M. et al. Pain empathy but not surprise in response to unexpected action explains arousal related pupil dilation." (VIPER database)
<p>This folder contains the 282 images of the "VIPER" database (visually-induced pain empathy repository) along with ratings of 24 independent raters. Details are described in the following publication:</p> <p>Paulus, F.M., Müller-Pinzler, L., Walper, D., Marx, S., Hamschmidt, L., Rademacher, L., Krach, S., Einhäuser, W. Pain empathy but not surprise in response to unexpected action explains arousal related pupil dilation.</p> <p>The material can be used for scientific purposes, provided this reference is appropriately cited. Please check the download site to get the up-to-date reference at the time of your publication.</p> <p> </p> <p>Conditions are identified by the filename of the image, which consists of the number of the scenario (1-83) and the condition identifier:<br> pain<br> neut(ral)<br> mism(atch)<br> tool<br> Note that the tool and the mismatch condition do not exist for all scenarios.</p> <p>The file ratings_viper.csv contains the ratings. Each image corresponds to a line, the columns are as follows:<br> Column 1: Filename of the image<br> Column 2: Scenario number<br> Column 3: condition<br> Columns 4 through 27: ratings of the 24 individuals (between 0 and 4, NaN if there was no rating recorded)</p> <p>The file thumbnail_viper.jpg provides an overview over all images in the database.</p> <p>For ease of download, the images are available as tar-archive (allImages_viper.tar) and as inidivual files.</p> <p> </p>
Resource-Rational Lossy-Context Surprisal (Model Predictions)
<p>Resource-Rational Lossy-Context Surprisal is a computationally implemented model of how humans process language, predicting at what points in complex sentences they experience comprehension difficulty. It unifies the memory-based and expectation-based paradigms in psycholinguistics, and provides a more refined account of when hierarchical structure is difficult to comprehend for humans.</p> <p>This repository contains output of the model on a battery of test sentences exhibiting iterated recursive structure, described in associated publications on Resource-Rational Lossy-Context Surprisal. The filenames are referred to in the source code, to be published together with a forthcoming journal publication on the model.</p> <p>The model was first described in the following publication:</p> <p><em>Lexical Effects in Structural Forgetting: Evidence for Experience-Based Accounts and a Neural Network Model</em></p> <p>(Michael Hahn, Richard Futrell, Edward Gibson), 33rd Annual CUNY Human Sentence Processing Conference, 2020</p>
Swordtail fish hybrids reveal that genome evolution is surprisingly predictable after initial hybridization
<p>Over the past two decades, biologists have come to appreciate that hybridization, or genetic exchange between distinct lineages, is remarkably common – not just in particular lineages but in taxonomic groups across the tree of life. As a result, the genomes of many modern species harbor regions inherited from related species. This observation has raised fundamental questions about the degree to which the genomic outcomes of hybridization are repeatable and the degree to which natural selection drives such repeatability. However, a lack of appropriate systems to answer these questions has limited empirical progress in this area. Here, we leverage independently formed hybrid populations between the swordtail fish <em>Xiphophorus birchmanni </em>and <em>X. cortezi </em>to address this fundamental question. We find that local ancestry in one hybrid population is remarkably predictive of local ancestry in another, demographically independent hybrid population. Applying newly developed methods, we can attribute much of this repeatability to strong selection in the earliest generations after initial hybridization. We complement these analyses with time-series data that demonstrates that ancestry at regions under selection has remained stable over the past ~40 generations of evolution. Finally, we compare our results to the well-studied <em>X. birchmanni×X. malinche </em>hybrid populations and conclude that deeper evolutionary divergence has resulted in stronger selection and higher repeatability in patterns of local ancestry in hybrids between <em>X. birchmanni </em>and <em>X. cortezi</em>.</p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 3.The chart of emotional/unemotional detection on the face in negative, positive and surprise states (Regular and magnified data)
<p>To evaluate the emotional/unemotional detection on the face, 328 tests were performed: 164 tests on the magnified data and 164 tests on the regular data. For this purpose, the train set includes the neutral state and only one of the emotional states (negativism, positivism and surprise) according to the test set. So that the train set includes regular data in 328 experiments. The experimental results are shown in Figure 3. </p>
Figure 4 in A surprising finding of Ecclisopteryx asterix Malicky, 1979 (Insecta, Trichoptera) in Croatia with notes to DNA barcoding and new distributional data of the subfamily Drusinae
Figure 4. Spatial distribution of the Drusinae species in Croatia, literature data supplemented with new records.
Figure 3 in A surprising finding of Ecclisopteryx asterix Malicky, 1979 (Insecta, Trichoptera) in Croatia with notes to DNA barcoding and new distributional data of the subfamily Drusinae
Figure 3. Maximum likelihood phylogram based on the COI sequences of Ecclisopteryx asterix from Croatia and haplotypes of Ecclisopteryx species available in BOLD database. Numbers at the nodes indicate maximum likelihood (ML) bootstrap support values (BS). Terminal codes represent BOLD Process IDs.
Figure 2. A in A surprising finding of Ecclisopteryx asterix Malicky, 1979 (Insecta, Trichoptera) in Croatia with notes to DNA barcoding and new distributional data of the subfamily Drusinae
Figure 2. A Larva of Ecclisiopteryx asterix in its case; B morphological character important for identification: shape of the pronotum (p); C pronotum (p), mesonotum (me), and sclerites at metanotum (mt) in dorsal view; D abdominal segments I-VI with gills (g) and lateral fringe (lf), right lateral view.
Figure 1. A in A surprising finding of Ecclisopteryx asterix Malicky, 1979 (Insecta, Trichoptera) in Croatia with notes to DNA barcoding and new distributional data of the subfamily Drusinae
Figure 1. A New record of Ecclisopteryx asterix Malicky, 1979 in Croatia (red dot) and hitherto known range in Austria, Slovenia and Italy (yellow area) (Robert, 2015); B the crenal reach of the Šumi Spring, and C a close up of the habitat where E. asterix larvae were collected.
Fig. 12 in A new large mammal from the Ypresian of Morocco: Evidence of surprising diversity of early proboscideans
Fig. 12. Character matrix of Daouitherium and other primitive lophodont proboscideans (see text: features 1–19) and most parsimonious cladogram resulting from parsimony analysis with Hennig86 program, with distribution of the derived features. Length = 56; CI = 85; RI = 82. This cladogram is unrooted. The significance of Daouitherium for the ancestral morphotype of proboscideans and the basal relationships of lophodont proboscidean taxa with respect to other proboscideans (e.g., Moeritherium, deinotheres) and tethytherians will be investigated separately with the study of the new material of Phosphatherium (work in preparation). Analysed features are additive and are weighted according to their relative importance (see matrix);howeverananalysisofthismatrixwithoutweightingthefeaturesdoes not change the resulting topology. Several reversions that are possible according to the algorithm have been discounted as being anatomically unlikely (features 1, 3, 4). Asterisk indicates convergent feature.
Fig. 9 in A new large mammal from the Ypresian of Morocco: Evidence of surprising diversity of early proboscideans
Fig. 9. Log transformed plot comparing the relative size (length × width) of the jugal teeth of Daouitherium and Numidotherium. After Court (1995: fig. 1). Note the slightly smaller size of Daouitherium and the strong size difference between m1 and m2. N. koholense is probably specialized in its large p2 with respect to p3 (feature 7).
Fig. 10 in A new large mammal from the Ypresian of Morocco: Evidence of surprising diversity of early proboscideans
Fig. 10. Comparison of the lower jugal dentition of Daouitherium rebouli gen. et sp. nov. and Numidotherium koholense. Occlusal sketches of the teeth. A. Daouitherium rebouli,CPSGMMA4,leftp2–4,m1–3,andalveoli for i1 or i2, i2 or i3, i3 or c1, c1 or p1. B. Numidotherium koholense, cast of unumbered specimen with left i1–2, diastema, p2–4, and m1–3. Drawings not to scale; scale bars 5 mm.
Fig. 11 in A new large mammal from the Ypresian of Morocco: Evidence of surprising diversity of early proboscideans
Fig. 11. Comparison of the upper premolar referred to Daouitherium rebouli gen. et sp. nov. with those of Phosphatherium and Numidotherium A. Phosphatherium escuilliei, holotype, P3–4. B. Phosphatherium escuilliei, PM18, P4. C. Daouitherium rebouli, CPSGM MA6, P4?. D. Numidotherium koholense, P3–4, unumbered cast. Occlusal sketches of the teeth. Drawings not proportional; scale bars 5 mm. CPSGM MA6 belongs to a noticeably small individual with respect to the hypodigm of Daouitherium rebouli.
Fig. 8 in A new large mammal from the Ypresian of Morocco: Evidence of surprising diversity of early proboscideans
Fig. 8. Daouitherium rebouli gen. et sp. nov. CPSGM MA6, left p4 in occlusal stereo−view. Anterior is up.
Fig. 7. Daouitherium rebouli gen. etsp.nov.MNHNPM3 in A new large mammal from the Ypresian of Morocco: Evidence of surprising diversity of early proboscideans
Fig. 7. Daouitherium rebouli gen. etsp.nov.MNHNPM3,rightdentary with with ascending ramus and m1–3, p3. in labial (A) and lingual (B) views.
Fig. 5 in A new large mammal from the Ypresian of Morocco: Evidence of surprising diversity of early proboscideans
Fig. 5. Daouitherium rebouli gen. et sp. nov. Drawing of m1–3, p2–4 preservedintheholotype,CPSGMMA4inlingual(A),labial(B),andocclusal (C) views. Scale bars 10 mm.
Fig. 6 in A new large mammal from the Ypresian of Morocco: Evidence of surprising diversity of early proboscideans
Fig. 6. Daouitherium rebouli gen. et sp. nov. MNHN PM3, right dentary with m1–3 and p3 in occlusal view.
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.