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.
1,254
datasets available to search
ShareScore release 0.9.0
Dataset results
1,254 results for “PANs”
First virtual endocast description of an early Miocene representative Pan-Octodontoidea (Caviomorpha, Hystricognathi) and considerations on the early brain evolution in South American rodents
<p><span>The study of the cranial endocast provides valuable information to understand the behavior of an organism since it coordinates sensory information and motor functions. In this work, we describe for the first time the anatomy of the encephalon of an early Miocene pan-octodontoid caviomorph rodent (<em>Prospaniomys</em> <em>priscus</em>) found in the Argentinean Patagonia, based on virtual 3D endocast. This fossil rodent has an endocast morphology here considered ancestral for Pan-Octodontoidea and also other South American caviomorph lineages, such as an encephalon with anteroposteriorly aligned elements, mesencephalon dorsally exposed, well-developed vermis of the cerebellum, rhombic cerebral hemispheres with well-developed temporal lobes. <em>Prospaniomys</em> also has relatively small olfactory bulbs, large paraflocculi of the cerebellum, low endocranial volume, and a degree of neocorticalization. Its EQ is lower compared with Paleogene North American and European non-caviomorph rodents, but slightly higher than several late early and late Miocene caviomorphs. The paleoneurological anatomical information supports the hypothesis that <em>Prospaniomys</em> was a generalist caviomorph rodent with terrestrial habits, and enhanced low-frequency auditory specializations. The scarce paleoneurological information indicates that several endocast characters in caviomorph rodents could change with ecological pressures. This work sheds light on the anatomy and evolution of several paleoneurological aspects of this particular group of South American rodents. </span></p>
Figure 16 in Neurocranial bones are key to untangling the sea cow evolutionary tree: osteology of the skull of Sobrarbesiren cardieli (Mammalia: Pan-Sirenia)
Figure 16. Synapomorphies common to all the most parsimonious trees mapped on the implied weighting consensus topology. The six new characters (223-228) described in the present study are mapped. See main text for descriptions of characters and states.
Figure 13 in Neurocranial bones are key to untangling the sea cow evolutionary tree: osteology of the skull of Sobrarbesiren cardieli (Mammalia: Pan-Sirenia)
Figure 13. Endocranial surfaces of two sirenian skulls. A, Kaupitherium bronni (sketch based on Voss & Hampe, 2017: figs 4, 11). B, Protosiren fraasi (sketch based on Sickenberg, 1934: plate I, fig. 4). For abbreviations, see the Material and Methods section.
Figure 14 in Neurocranial bones are key to untangling the sea cow evolutionary tree: osteology of the skull of Sobrarbesiren cardieli (Mammalia: Pan-Sirenia)
Figure 14. Comparison of basicranial region of various Eocene sirenian skulls. A, Prorastomus sirenoides (sketch based on holotype BMNH 44897). B, Sobrarbesiren cardieli (MPZ 2017/1). C, Eotheroides aegyptiacum (sketch based on Abel, 1913: table (II) XXXI, fig. 2). D, 'Halitherium' taulannense (sketch based on holotype RGHP D040). E, Protosiren fraasi (sketch based on the BMNH skull cast PV M 9367). For abbreviations, see the Material and Methods section.
Figure 9 in Neurocranial bones are key to untangling the sea cow evolutionary tree: osteology of the skull of Sobrarbesiren cardieli (Mammalia: Pan-Sirenia)
Figure 9. Bones of the ear region of Sobrarbesiren cardieli. A, right periotic of the paratype skull MPZ 2017/2 in ventral view. B, C, MPZ 2020/607, right pars temporalis (= tegmen tympani) in ventral (B) and dorsal (C) views. D, E, right tympanic of the holotype skull MPZ 2017/1 in lateral (D) and medial (E) views. Dashed areas denote broken surfaces. For abbreviations, see the Material and Methods section.
Figure 8 in Neurocranial bones are key to untangling the sea cow evolutionary tree: osteology of the skull of Sobrarbesiren cardieli (Mammalia: Pan-Sirenia)
Figure 8. Isolated squamosals and jugal of Sobrarbesiren cardieli. A–E, MPZ 2020/603, right isolated squamosal; cranial portion in lateral view (A), interpretive sketch (B), and in posterior view (C); and zygomatic process in lateral (D) and ventral (E) views. F–K, MPZ 2020/604, right isolated juvenile squamosal in lateral (F), ventral (G) and medial (H) views, and interpretative sketches (I–K, respectively). L–O, MPZ 2020/606, left partial jugal in lateral (L) and medial (N) views, and interpretative sketches (M, O, respectively). Dashed lines indicate incomplete bones, and dashed areas denote broken surfaces. For abbreviations, see the Material and Methods section.
Figure 4 in Neurocranial bones are key to untangling the sea cow evolutionary tree: osteology of the skull of Sobrarbesiren cardieli (Mammalia: Pan-Sirenia)
Figure 4. Skulls of Sobrarbesiren cardieli in dorsal view. A, paratype skull MPZ 2017/2. B, interpretative sketch of the paratype skull. C, holotype skull MPZ 2017/1. D, skull MPZ 2020/591. Sutures are marked with continuous lines. Dashed lines indicate incomplete bones, and dashed areas denote broken surfaces. For abbreviations, see the Material and Methods section.
Figure 2 in Neurocranial bones are key to untangling the sea cow evolutionary tree: osteology of the skull of Sobrarbesiren cardieli (Mammalia: Pan-Sirenia)
Figure 2. Skulls of Sobrarbesiren cardieli in ventral view. A, paratype skull MPZ 2017/2. B, interpretative sketch of the paratype skull. C, holotype skull MPZ 2017/1. D, skull MPZ 2020/591. Sutures are marked with continuous lines. Dashed lines indicate incomplete bones, and dashed areas denote broken surfaces. For abbreviations, see the Material and Methods section.
Figure 5 in Neurocranial bones are key to untangling the sea cow evolutionary tree: osteology of the skull of Sobrarbesiren cardieli (Mammalia: Pan-Sirenia)
Figure 5. Frontal bones assigned to immature individuals of Sobrarbesiren cardieli. A–F, MPZ 2020/593, left frontal in dorsal (A), ventral (B) and lateral (C) views, and interpretative sketches (D–F, respectively). G–I, MPZ 2020/592, left frontal in dorsal (G), ventral (H) and lateral (I) views. J–L, MPZ 2020/594, left frontal in dorsal (J), ventral (K) and lateral (L) views. Dashed lines indicate incomplete parts. For abbreviations, see the Material and Methods section.
Figure 7 in Neurocranial bones are key to untangling the sea cow evolutionary tree: osteology of the skull of Sobrarbesiren cardieli (Mammalia: Pan-Sirenia)
Figure 7. Skulls of Sobrarbesiren cardieli in posterior view. A, paratype skull MPZ 2017/2. B, interpretative sketch of the paratype skull. C, holotype skull MPZ 2017/1. Sutures are marked with continuous lines. Dashed lines indicate incomplete bones, and dashed areas denote broken surfaces. For abbreviations, see the Material and Methods section.
Figure 11 in Neurocranial bones are key to untangling the sea cow evolutionary tree: osteology of the skull of Sobrarbesiren cardieli (Mammalia: Pan-Sirenia)
Figure 11. Mandibular fragment of Sobrarbesiren cardieli (MPZ 2020/608) in dorsal (A) and medial (B) views. For abbreviations, see the Material and Methods section.
Figure 10 in Neurocranial bones are key to untangling the sea cow evolutionary tree: osteology of the skull of Sobrarbesiren cardieli (Mammalia: Pan-Sirenia)
Figure 10. Schematic drawings of tympanic bones of various sirenian taxa. A, Sobrarbesiren cardieli, right tympanic of the holotype skull MPZ 2017/1. B, Prorastomus sirenoides, right tympanic BMNH 44897. C, Eotheroides lambondrano, left tympanic (from Samonds et al., 2009: fig. 6B). D, Metaxytherium albifontanum, right tympanic (from Vélez-Juarbe & Domning, 2014: fig. 8A). E, Trichechus senegalensis, right tympanic (from Robineau, 1969: fig. 11, not to scale). For abbreviations, see the Material and Methods section. Measurements: APL, Anteroposterior length; DVH, Dorsoventral height; ID, Internal diameter.
Figure 3 in Neurocranial bones are key to untangling the sea cow evolutionary tree: osteology of the skull of Sobrarbesiren cardieli (Mammalia: Pan-Sirenia)
Figure 3. Isolated right nasal process of the premaxilla of Sobrarbesiren cardieli (MPZ 2017/3) in dorsal (A) and medial (B) views. For abbreviations, see the Material and Methods section.
Figure 1 in Neurocranial bones are key to untangling the sea cow evolutionary tree: osteology of the skull of Sobrarbesiren cardieli (Mammalia: Pan-Sirenia)
Figure 1. Skulls of Sobrarbesiren cardieli in lateral view. A, paratype skull MPZ 2017/2. B, interpretative sketch of the paratype skull. C, holotype skull MPZ 2017/1. D, skull MPZ 2020/591. Sutures are marked with continuous lines. Dashed lines indicate incomplete bones, and dashed areas denote broken surfaces. For abbreviations, see the Material and Methods section.
Figure 6 in Neurocranial bones are key to untangling the sea cow evolutionary tree: osteology of the skull of Sobrarbesiren cardieli (Mammalia: Pan-Sirenia)
Figure 6. Endocranial structures of Sobrarbesiren cardieli. Skull roof of skull MPZ 2020/591 (A) and MPZ 2017/2 (B) in ventral view. C–F, MPZ 2020/598, parietal–supraoccipital skullcap of a juvenile individual in posterior (C) and ventral (E) views, and interpretative sketches (D, F, respectively). For abbreviations, see the Material and Methods section.
Figure 12 in Neurocranial bones are key to untangling the sea cow evolutionary tree: osteology of the skull of Sobrarbesiren cardieli (Mammalia: Pan-Sirenia)
Figure 12. Isolated teeth of Sobrarbesiren cardieli. A, B,?I1 (MPZ 2017/4) in occlusal (A) and labial (B) views. C, D,?I3 (MPZ 2017/5) in occlusal (C) and lateral (D) views. E–G, P2 (MPZ 2020/610) in occlusal (E), labial (F) and lingual (G) views. H–J,?P3–4 (MPZ 2020/611) in occlusal (H), labial (I) and lingual (J) views. K–M, deciduous premolar (MPZ 2020/614) in occlusal (K), labial (L) and lingual (M) views.
ECFAS Pan-EU Flood Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211), https://www.ecfas.eu/
<p>The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.</p> <p>The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the <strong>European Commission H2020 Framework Programme</strong> within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services. </p> <p><em><strong>Reference literature:</strong></em></p> <p><em><strong>Le Gal, M., Fernández-Montblanc, T., Duo, E., Montes Perez, J., Cabrita, P., Souto Ceccon, P., Gastal, V., Ciavola, P., and Armaroli, C.: A new European coastal flood database for low–medium intensity events, Nat. Hazards Earth Syst. Sci., 23, 3585–3602, <a href="https://doi.org/10.5194/nhess-23-3585-2023">https://doi.org/10.5194/nhess-23-3585-2023</a>, 2023.</strong></em></p> <p><strong>Description of the Dataset</strong></p> <p>The present database gathers flood and velocity maps for the European Union coast as well as their associated forcing parameters. The coast is divided into geographic regions embracing similar oceanographic conditions and subsequently into coastal sectors. The coastal sectors can be identified by its region index RXXX and its own index CSYYY. For each coastal sector, flood models were developed using the LISFLOOD-FP model with a grid resolution of 100 m. The flood model configuration follows the recommendation highlighted in ECFAS Deliverable D5.2 - Validated LISFLOOD-FP model for coastal areas. The flood and velocity maps are associated with synthetic storms that are characterised by a specific extreme water level and storm duration. These parameters were derived from Extreme Value Analyses performed on the ECFAS ANYEU-SSL hindcast (ECFAS D4.1 - Report on the calibration and validation of hindcasts and forecasts of TWL and D4.3 - Report on the identification of local thresholds of TWL for triggering coastal flooding). Five extreme water level values for each coastal point of the hindcast, and three durations (12, 24 and 36 h) were identified leading to 15 scenarios for each coastal sector. The flood and velocity maps are gathered into a NetCDF file for each coastal sector indicating the scenario parameters as attributes. In addition, the extreme water level values used for each coastal sector are contained in a complementary NetCDF file.</p> <p>The shapefile of the polygons defining the coastal sectors as defined for the catalogue implementation is included in the database.</p> <p><strong>- The ECFAS Flood Catalogue was used to produce the associated ECFAS Pan-EU Impact Catalogue:</strong></p> <p><strong>Impact Catalogue in Zenodo</strong>: Duo, E., Montes Pérez, J., Le Gal, M., Souto Ceccon, P.E., Cabrita, P., Fernández Montblanc, T., and Ciavola, P., 2022. ECFAS Pan-EU Impact Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211). <a href="http://www.ecfas.eu/">www.ecfas.eu</a> [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.6778864">https://doi.org/10.5281/zenodo.677865</a></p> <p><em><strong>Impact Catalogue Reference literature</strong>: Duo, E., Montes, J., Le Gal, M., Fernández-Montblanc, T., Ciavola, P., and Armaroli, C.: Validated probabilistic approach to estimate flood direct impacts on the population and assets on European coastlines, Nat. Hazards Earth Syst. Sci., 25, 13–39, <a href="https://doi.org/10.5194/nhess-25-13-2025">https://doi.org/10.5194/nhess-25-13-2025</a>, 2025.</em></p> <p> </p> <p>The Flood Catalogue is accompanied by a technical document describing methods, datasets, structure, format and content of the ECFAS Flood and Impact Catalogues:</p> <p>Duo, E., Le Gal, M., Souto Ceccon, P.E., Montes Pérez, J., 2022. <a href="https://ec.europa.eu/research/participants/documents/downloadPublic?documentIds=080166e5ee287a5d&appId=PPGMS">Technical document</a> on the ECFAS Flood and Impact Catalogue, D5.4 – Pan-EU flood maps catalogue - ECFAS project (GA 101004211). <a href="http://www.ecfas.eu/">www.ecfas.eu</a></p> <p> </p> <p>This ECFAS <strong>Flood Catalogue</strong> is made available under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/odbl/1.0/">http://opendatacommons.org/licenses/odbl/1.0/</a>. Any rights in individual contents of the Flood Catalogue are licensed under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/dbcl/1.0/">http://opendatacommons.org/licenses/dbcl/1.0/</a>.</p> <p>This <strong>technical document</strong> describing methods, datasets, structure, format and content of the ECFAS Flood and Impact Catalogues is made available under the <strong>Creative Commons Attribution 4.0 International License</strong>.</p> <p>*The size of the uncompressed dataset is 124 GB.</p> <p> </p> <p><em><strong>Disclaimer:</strong></em></p> <p>ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.</p> <p>This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211</p> <p> </p>
QB and WV3 Datasets for Pan-Sharpening
<p>We follow Wald’s protocol to spatially degrade the MS and PAN images by a factor of the spatial-resolution gap<br> between the PAN and LRMS images.</p>
eye-tracking data from a survey on zooming in a pan-scalar map
<p><strong>Recording and processing a survey using an eye tracker </strong></p> <p>The eye-tracker used is a Pupil Core from Pupil Labs. The basic eye tracker configuration, i.e. a fixation time of 80 ms to 200 ms, is kept for this experiment.</p> <p> </p> <p>The aim of the experiment is to understand what a person looks at to find their way around a multi-scale map and to understand the different strategies used. To do this, the user will be free to use the map as he wishes, i.e. he can use pan and zoom at will. Four types of tasks will be asked in order to have a maximum of types of use of multi-scale map.The first task is to simulate that a user is using an application like map or Google map and is looking for a specific address. The map application will then zoom in very strongly on the address. The user has little spatial context and it often takes some time to find his way around. To simulate the application, a point is placed on Paris or its surroundings and the display is very zoomed (Paris was chosen because most people have a more or less detailed mental map of Paris). The user is then asked to interact with the map (zooming and panning) until he feels he is sufficiently located, as he would if he had to search for a place on his mobile phone. When he is located, he just needs to move on to the next stage without asking for validation. This stage is carried out in four locations. The four points are located near Montmartre, at the entrance to the catacombs of Paris, in Vincennes and finally at Porte d'Asnières</p> <p><br> The second task is to find a place from an aerial image. The aerial image of a specific area is displayed and the map is zoomed out to the city where the location is located. The user must then try to find the location in the image. Unlike the first task, the user must request validation before proceeding to the next stage.<br> This task is repeated in two different cities. The two images are the tête d'or park in Lyon and a building block next to a railway in Dijon.</p> <p><br> The third task also consists of finding a precise location using textual indications. The user still has to ask for validation to go to the next stage .</p> <p>This task is repeated in two different cities.The first was "to find the town hall which is just south of the town centre and next to the library" and the second was "to find the stadium east of the town centre and north of the river Vilaine with a north/south orientation.</p> <p><br> The last task builds on tasks 2 and 3. The map is again zoomed out, an aerial image appears and textual indications are given. This task is repeated on two different cities.</p> <p>The first image is of a building in beauvais with the indication: "the building is in the north west of sqare next to the SNCF station". The second one is a picture of a stadium in lyon with the indication: "the stadium is west of the confluence of lyon".</p> <p><strong>data format :</strong><br> <strong>Coord_fixation_on_map_x_y</strong>: geolocated fixation point with x the survey type 1 or 2 and y the candidate number (id_fixation,x,y,zoom,etape)</p> <p><strong>Pan</strong>: pan on the map during the survey</p> <p><strong>Pan_fixation_on_map</strong> : fixation during a pan</p> <p><strong>zoom</strong>: zoom on the map during the survey</p> <p><strong>zoom_fixation_on_map</strong>: fixation during a zoom</p> <p><strong>stat</strong>: number of zoom, pan and fixation per step</p> <p><strong>result_map_x </strong>= map status every 100 ms during the survey x</p> <p><strong>00x </strong>: export file of the eye-tracker pupil Lab</p> <p> </p>
Pan-Tumor T-Lymphocyte Detection Dataset
<p>This is a pan-tumor immunohistochemistry dataset containing 2 mm<sup>2</sup>-sized regions of interest (ROIs) from 92 whole slide images stained for cluster of differentiation 3 (CD3, antibody clone SP7). The dataset covers four tumor indications:</p> <ul> <li>32 head and neck squamous cell carcinoma (HNSCC) samples</li> <li>20 non-small cell lung cancer (NSCLC) samples</li> <li>20 triple-negative breast cancer (TNBC) samples</li> <li>20 gastric cancer (GC) samples</li> </ul> <p>The samples were digitized at a resolution of 0.23 μm/px (40× objective lens) using the NanoZoomer 2.0-HT scanning system (Hamamatsu, Japan). All samples were prepared and digitized at Merck Healthcare KGaA.</p> <p>We provide bounding box annotations for three cell types: CD3+ immune cells, tumor cells, and other cells. For each tumor indication, five ROIs were manually annotated by three pathologists. For these ROIs, we provide all three annotations and the consensus of all raters. All other ROIs have been semi-automatically annotated using commercial image analysis software. </p>
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.