Find research datasets worth reusing

opencc-by-4.0Dec 2020View details →

Plate model for 'Extending Full-Plate Tectonic Models into Deep Time: Linking the Neoproterozoic and the Phanerozoic '

Plate model for the last 1 Ga accompanying Merdith et al. (2021), Earth Science Reviews. -changes Correction to animation file.

opencc-by-4.0Nov 2024View details →

A Real-Time Eye-Tracking Dataset for Autism Severity Classification Using Deep Learning

Eye-Tracking (ET) technologies have shown significant potential in autism research, providing critical insights into gaze patterns and their correlation with autism severity. However, a persistent challenge in developing Deep Learning (DL) models for ET analysis is the lack of publicly available, annotated datasets tailored for specific tasks. In order to close this gap, we present a novel, meticulously annotated resource designed to classify autism severity based on ET data. This dataset consists of 4,000 high-resolution (416×416 pixels) eye images derived from video recordings of 40 participants, evenly distributed across four autism severity groups: low, mild, medium, and high. Each participant's video was processed to extract 50 frames per session, capturing diverse gaze behaviors such as fixations, saccades, and smooth pursuits. Both left and right eye images were segmented from these frames, yielding 100 images per participant and ensuring balanced representation across severity categories (1,000 images per group). The dataset is annotated with detailed metadata, including subject ID, frame number, autism severity level, and eye type (left or right), providing a robust foundation for precise feature extraction and analysis. Facilitating its application in DL model development, this dataset addresses a critical gap in the limited availability of ET datasets. It provides a robust benchmark for autism severity classification, establishing a foundational resource for advancing Machine Learning(ML) research in the domain of autism. This dataset serves as a critical resource for advancing ET-based classification models, fostering accurate and efficient assessment of autism severity, and supporting broader autism research.

opencc-by-4.0Feb 2022View details →

Climatology of deep O+ dropouts in the night-time F-region in solar minimum measured by a Langmuir Probe onboard the International Space Station

Dataset contains data pertaining to an accepted JGR Space Physics article of the same name as the dataset. The link to the article is the following: <a href="https://doi.org/10.1029/2022JA030446">https://doi.org/10.1029/2022JA030446</a>. The dataset contains the high level data that were used to generate Figs 2-5 in the aforementioned paper.   The observations recorded by ISS FPMU will be uploaded to NASA SPDF as well. A previous dataset already exists in CDAweb under ISS/FPMU. The O+ information will be added with the new upload. For any questions about the data or the tools used to derive the figures from the data, please take a look at the paper <a href="https://doi.org/10.1029/2022JA030446">https://doi.org/10.1029/2022JA030446</a>, or contact Shantanab Debchoudhury at debchous@erau.edu.

opencc-by-4.0Aug 2022View details →

Orogens of Big Sky Country: Reconstructing the Deep-Time Tectonothermal History of the Beartooth Mountains, Montana and Wyoming, USA (Supporting Information)

Supporting datasets for Ronemus et al., "Orogens of Big Sky Country: Reconstructing the Deep-Time Tectonothermal History of the Beartooth Mountains, Montana and Wyoming, USA" in review at Tectonics as of August 16, 2022. Dataset S1. Detailed analytical settings and data for zircon U-Pb geochronology Dataset S2. Biotite 40Ar/39Ar analytic results Dataset S3. Zircon (U-Th)/He analytic results Dataset S4. Apatite and zircon grain photomicrographs with measurements Dataset S5. Apatite (U-Th-Sm)/He analytic results Dataset S6. QTQt input and results files   Datasets S1, S2, S3, and S5 are also available in the Tectonics submission supporting information.

opencc-by-4.0Dec 2020View details →

Semantic Segmentation of Time Series Imagery Using Deep Convolutional Neural Networks: A Case Study of Sandbars in Grand Canyon

This dataset contains imagery used to train and test Deep Convolutional Neural Networks for the purpose of binary semantic segmentation of a time series of oblique imagery capturing sandbar monitoring sites in The Grand Canyon. In addition the scripts needed for removing image distortion, registering, rectifying, and labeling imagery is present.

opencc-zeroOct 2023View details →

Data from: Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning

<div> <div> Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing algorithms which are not fully understood and utilized. In the paper "Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning", we propose a novel method based on deep learning for timing analysis of modularized detectors without explicit needs of labelling event data. By taking advantage of the intrinsic time correlations, a label-free loss function with a specially designed regularizer is formed to supervise the training of neural networks towards a meaningful and accurate mapping function. We mathematically demonstrate the existence of the optimal function desired by the method, and give a systematic algorithm for training and calibration of the model. The proposed method is validated on two experimental datasets based on silicon photomultipliers (SiPM) as main transducers: <ol> <li>In the toy experiment, we collect data from a pair of SiPM sensors from a common laser source. The neural network model achieves the single-channel time resolution of 8.8 ps and exhibits robustness against concept drift in the dataset. </li> <li>In the electromagnetic calorimeter experiment, we collect data from an eight-channel calorimeter module. Several neural network models (Fully-Connected, Convolutional Neural Network and Long Short Term Memory) are tested to show their conformance to the underlying physical constraint and to judge their performance against traditional methods. </li> </ol> In total, the proposed method works well in either ideal or noisy experimental condition and recovers the time information from waveform samples successfully and precisely. The dataset in this repository serves as a basis for similar researches on timing performance of SiPM-based nuclear detectors, and on application of neural networks to typical signals of nuclear radiation detectors. </div> </div>

opencc-zeroFeb 2024View details →

Data from: Ancient and modern genomes reveal microsatellites maintain a dynamic equilibrium through deep time

Microsatellites are widely used in population genetics, but their evolutionary dynamics remain poorly understood. It is unclear whether microsatellite loci drift in length over time. This is important because the mutation processes that underlie these important genetic markers are central to the evolutionary models that employ microsatellites. We identify more than 27 million microsatellites using a novel and unique dataset of modern and ancient Adélie penguin genomes along with data from 63 published chordate genomes. We investigate microsatellite evolutionary dynamics over two time scales: one based on Adélie penguin samples dating to approximately 46.5 kya, the other dating to the diversification of chordates more than 500 Mya. We show that the process of microsatellite allele length evolution is at dynamic equilibrium; while there is length polymorphism among individuals, the length distribution for a given locus remains stable. Many microsatellites persist over very long time scales, particularly in exons and regulatory sequences. These often retain length variability, suggesting that they may play a role in maintaining phenotypic variation within populations.

opencc-zeroOct 2021View details →

How to render species comparable taxonomic units through deep time: A case study on intraspecific osteological variability in extant and extinct lacertid lizards

Generally, the species is considered to be the only naturally occurring taxon. However, species recognized and defined using different species delimitation criteria cannot readily be compared, impacting studies of biodiversity through Deep Time. This comparability issue is particularly marked when comparing extant with extinct species because the only available data for species delimitation in fossils are derived from their preserved morphology, which is generally restricted to osteology in vertebrates. Here, we quantify intraspecific, intrageneric, and intergeneric osteological variability in extant species of lacertid lizards using pairwise dissimilarity scores based on a data set of 253 discrete osteological characters for 99 specimens referred to 24 species. Variability is always significantly lower intraspecifically than between individuals belonging to distinct species of a single genus, which is in turn significantly lower than intergeneric variability. Average values of intraspecific variability and associated standard deviations are consistent (with few exceptions), with an overall average within a species of 0.208 changes per character scored. Application of the same methods to six extinct lacertid species (represented by 40 fossil specimens) revealed that intraspecific osteological variability is inconsistent, which can at least in part be attributed to different researchers having unequal expectations of the skeletal dissimilarity within species units. Such a divergent interpretation of intraspecific and interspecific variability among extant and extinct species reinforces the incomparability of the species unit. Lacertidae is an example where extant species recognized and defined based on a number of delimitation criteria show comparable and consistent intraspecific osteological variability. Here, as well as in equivalent cases, application of those skeletal dissimilarity values to paleontological species delimitation potentially provides a way to ameliorate inconsistencies created by the use of morphology to define species.

Fig. 10 in An exceptionally preserved Sphenodon-like sphenodontian reveals deep time conservation of the tuatara skeleton and ontogeny

Fig. 10 Evolutionary changes on the temporal region of sphenodontians. Many early-evolving lepidosaurs retain a single temporal fenestration as the jugal (cyan) does not contact the quadrate/quadratojugal (green) posteriorly. Afull development of the lower temporal bar and double temporal fenestration evolved independently at least twice in sphenodontians, once among clevosaurids and once in sphenodontines—an adaptation for stabilizing the quadrate and reducing overall stress in the skull during hard biting58. The latter is unique among lepidosaurs by including contributions from both the jugal and quadratojugal—a morphology convergent with many non-lepidosaurian early diapsid reptiles. Red circle, complete lower temporal bar; blue circle: incomplete lower temporal bar; blue-red gradient circle, lower temporal bar incomplete in juveniles but complete in adults. Skull drawings from top to bottom: Gephyrosaurus (drawn by TRS based on ref. 20), Megachirella (re-drawn by TRS from ref. 33), Diphydontosaurus (drawn by TRS based on ref. 20), Clevosaurus (drawn by TRS based on ref. 20), Palaeopleurosaurus (drawn by TRS based on ref. 20), Navajosphenodon (drawn by A. Brum), and Sphenodon (drawn by TRS based on MCZ R4702).

Fig. 9 in An exceptionally preserved Sphenodon-like sphenodontian reveals deep time conservation of the tuatara skeleton and ontogeny

Fig. 9 Phylomorphospace of early lepidosaurs and sphenodontians using discrete morphological characters. Clade "A" refers to the clade recovered by the final analysis (relaxed morphological clock Bayesian inference) both here and in Simões, et al.19, including Homeosaurus, pleurosaurids, and saphaeosaurids. For figures with individual taxon names, see Supplementary Fig. 4. Cyno Cynosphenodon, Kaw Kawasphenodon, Nav Navajosphenodon, Sphe. Sphenodon, Sphf. Sphenofontis.

Fig. 8 in An exceptionally preserved Sphenodon-like sphenodontian reveals deep time conservation of the tuatara skeleton and ontogeny

Fig. 8 Majority rule consensus tree from the relaxed morphological clock Bayesian inference analysis with tip dating. Results indicate the phylogenetic relationships among sphenodontians highlighting the placement of N. sani (in bold), clade posterior probabilities (top node values in bold), and median divergence times (bottom node values). Purple node error bars represent the 95% highest posterior density estimates for divergence times. Skull illustrations (all photos taken by TRS) for each major clade are, from top to bottom, Megachirella wachtleri (Squamata), Clevosaurus brasiliensis (Clevosauridae), Homeosaurus maximiliani (Homeosaurinae), Pleurosaurus gingsburi (Pleurosauridae), Kallimodon pulchellus, Priosphenodon avelasi (Eilenodontinae), and Sphenodon punctatus (Sphenodontinae).

Fig. 7 in An exceptionally preserved Sphenodon-like sphenodontian reveals deep time conservation of the tuatara skeleton and ontogeny

Fig. 7 Postcranial skeleton of the holotype of N. sani (MNA.V.12442). a Atlas, axis, and cervical vertebrae 3 and 4 in ventral view. b Part of Atlas–axis complex in dorsal view. c Dorsal vertebrae in ventral view. d Caudal vertebrae in lateral view. e Pelvic girdle and pygals in ventrolateral view. f Right pes in partial articulation. g Left pes with only distal phalanges preserved. h Left scapula and coracoid in lateral view. i Right(?) pubis and ilium. j Left forearm in posterior view. k Left forearm in anterior view. l Right forearm in anterior view (slightly displaced radius and ulna). m Right forearm in posterior view. Ace acetabulum, As astragulus, Ax axis, Ax.Ce. axis pleurocentrum, Ax.NS. axis neural spine, At.NA. atlas neural arches, Ca.V. caudal vertebra, Co coracoid, Ce.V. cervical vertebra, Dp.Cr. deltopectoral crest, Ent.Fr. entepicondylar foramen, H humerus, H.H. humeral head, Il ilium, Il.Bl. iliac blade (partially preserved), Is isquium, N.S. neural spine, Obt.Fr. obturator foramen, Od. odontoid (atlas centrum), Ol.Pr. olecranon process, Pn.Ph. penultimate phalanx, Po.No. posterior notch on acetabulum, Pu pubis, Pu.Pr. anterior pubic process, Py pygals, Ra radius, Ra.Cd. radial condyle, Sca scapula, Ul ulna, V.Cr. midventral crest, I–V digit number. Scale bars = 1 mm.

Fig. 6 in An exceptionally preserved Sphenodon-like sphenodontian reveals deep time conservation of the tuatara skeleton and ontogeny

Fig. 6 Conservation of ontogenetic stages from Navajosphenodon to Sphenodon. Ontogenetic series on the maxilla (a–c) and dentary (d–f) of Sphenodon punctatus (rescaled to the same length). Ontogenetic series on the maxilla (g–i) and dentary (j–l) of Navajosphenodon sani. Specimen numbers: FMNH 207433 (b, e); FMNH 11113 (c, f); MCZ VP VP 9094 (g); MCZ VP VP 9100 (h); MCZ VP VP 9093 (i); MCZ VP VP 101564 (top left), MCZ VP VP 9094 (top right), MCZ VP VP 9099 (bottom left), MCZ VP VP 101569 (bottom right) (j); MNA.V.12442 (k); MCZ VP 9093 (l). a, d Re-drawn from ref. 43. Add.T. additional teeth, Alt.T. alternating teeth, Ang angular, D.C.Pr. dentary coronoid process, D.Po.Pr. dentary posterior process, Ed. edentulous region, Hat.T. Hatchling teeth, M.Pr. mentonian process, Me.C. Meckelian canal, Suc.T. Successional teeth, Worn worn out teeth. Scale bars = 1 mm.

Fig. 5 in An exceptionally preserved Sphenodon-like sphenodontian reveals deep time conservation of the tuatara skeleton and ontogeny

Fig. 5 Mandibles of the holotype of N. sani (MNA.V.12442). Left mandible in lateral view (a) and medial view (b). Right dentary in medial view (c). Left post-dentary bones in dorsal view (d). Ce.Rd. central ridge, Gl. glenoid articulation, Me.C. Mekelian canal, RAP retroarticular process, San.C.Pr. surangular coronoid process, Sym. symphysis, V.Cr. ventral crest. Scale bars = 10 mm (a, b) and 1 mm (c, d).

Fig. 4 in An exceptionally preserved Sphenodon-like sphenodontian reveals deep time conservation of the tuatara skeleton and ontogeny

Fig. 4 Palatal region and braincase of the holotype of N. sani (MNA.V.12442). Palatal region in dorsal (a) and ventral (b) views. Basisphenoid in ventral (c), dorsal (d), anterior (e), and right lateral (f) views. Arc.Fl. Arcuate flanges, Bpt.Pr. basipterygoid process, Ca.Op. openings for internal carotid arteries, Cl. Pr. clinoid process, Cu.Pr. cultriform process, Do.Se. dorsal sella, L.Pr. lateral process, Pal.Asc.Pr. palatine ascending process, Pal.T. palatine teeth, Ptg.T. pterygoid teeth, Q.Pr. quadrate process, Sel.Tu. sella turcica, Tr.Cr. trabeculae cranii, VI passage for cranial nerve VI (abducens canal). Scale bars = 1 mm.

Fig. 2 in An exceptionally preserved Sphenodon-like sphenodontian reveals deep time conservation of the tuatara skeleton and ontogeny

Fig. 2 Micro CT-scanned and fully segmented skull and mandibles of the holotype of N. sani (MNA.V.12442). a Right ventrolateral view. b Left dorsolateral view. Art articular, Boc basioccipital, Bsp basisphenoid, C coronoid, CbI first ceratobranchial, D dentary, Ect ectopterygoid, Epi epipterygoid, F frontal, J jugal, M maxilla, N nasal, P parietal, Pal palatine, PFr postfrontal, PM premaxilla, Po postorbital, Pra prearticular, PrF prefrontal, Ptg pterygoid, Q-Qj quadrate-quadratojugal, San surangular, Spm septomaxilla, Sq squamosal, (l) left side and (r) right side. Scale bar = 1 mm.

Fig. 3 in An exceptionally preserved Sphenodon-like sphenodontian reveals deep time conservation of the tuatara skeleton and ontogeny

Fig. 3 Individual elements of the skull of the holotype of N. sani (MNA.V.12442). a Premaxilla in lateral (left) and posterior (right) views. b Left maxilla in lateral (left) and medial (right) views. c Left maxilla in occlusal view. d Nasals in dorsal view. e Left prefrontal in lateral (left) and medial (right) views. f Right septomaxilla in ventral view. g Right postfrontal in lateral (left) and medial (right) views. h Right jugal and associated quadratojugal in lateral view. i Fused left quadrate–qudratojugal complex in posterior view. j Left jugal in lateral view. k Right postorbital in medial view. l Left postorbital and squamosal in lateral view. m Frontals and parietals in dorsal view. n Frontals in ventral view. o Right frontal in lateral view. Relative bone positions in h, l, m are as preserved in the specimen. Ant.Pr. anterior process, Av.Pr. anteroventral process, Acr.T. acrodont teeth, Ant.Pr. anterior process, AnL.Pr. anterolateral process, br. broken edge, D.Pr. dorsal process, Di.Pr. distal process, E.N. external nares, J.Ft. facet for jugal, N.Pr. nasal process, P.Pr. posterior process, Pal.Ft. facet for maxillary process of palatine, PFr.Ft. facet for postfrontal, Pm.Cr. posteromedial crest, Pm.Pr. premaxillary process, Po.Ft. facet for postorbital, Po.Pr. postorbital process, PoL.Pr. posterolateral process, PrF.Ft. facet for prefrontal, Pv.Pr. posteroventral process, Q quadrate, Qj quadratojugal, Qj.Fr. quadratojugal foramen, Sof.Pr. subolfactory process, St.Pr. supratemporal process, T.Ap. tooth apex, V.Pr. ventral process, Vl.Pr. ventrolateral process, Vo.Ft. facet for vomeronasal organ. Scale bars = 1 mm.

opencc-zeroApr 2022View details →

Data from: Phylogenomics of elongate-bodied Springtails reveals independent transitions from aboveground to belowground habitats in deep time

Soil has become a major hotspot of biodiversity studies, yet the pattern and timing of the evolution of soil organisms are poorly known because of the scarcity of palaeontological data. To overcome this limitation, we conducted a genome-based macroevolutionary study of an ancient, diversified, and widespread lineage of soil fauna, the elongate-bodied springtails (class Collembola, order Entomobryomorpha). To build the first robust backbone phylogeny of this previously refractory group, we sampled representatives of major higher taxa (6 out of 8 families, 11 out of 16 subfamilies) of the order with an emphasis on the most problematic superfamily Tomoceroidea, applied whole-genome sequencing (WGS) methods, and compared the performance of different combinations of datasets (universal single-copy orthologues/USCO versus ultraconserved elements/UCE) and modelling schemes. The fossil-calibrated timetree was used to reconstruct the evolution of body size, sensory organs, and pigmentation to establish a time frame of the ecomorphological divergences. The resultant trees based on different analyses were congruent in most nodes. Several discordant nodes were carefully evaluated by considering method fitness, morphological information, and topology test. The evaluation favoured the well-resolved topology from analyses using USCO amino acid matrices and complex site-heterogeneous models (CAT+GTR and LG+PMSF (C60)). The preferred topology supports the monophyletic superfamily Tomoceroidea as an early-diverging lineage and a sister relationship between Entomobryoidea and Isotomoidea. The family Tomoceridae was recovered as monophyletic, while Oncopoduridae was recovered as paraphyletic, with Harlomillsia as a sister to Tomoceridae and hence deserving a separate family status as Harlomillsiidae Yu and Zhang fam. n. Ancestral Entomobryomorpha were reconstructed as surface-living, supporting independent origins of soil-living groups across the Palaeozoic–Mesozoic, and highlighting the ancient evolutionary interaction between aboveground and belowground fauna.

opencc-by-4.0May 2022View details →

A tempοral Deep Convolutional Neural Network model on Sentinel-1 Image Time Series for pixel-wise Flood Classification (dataset)

This is a dataset which has been designed to be used for flood time series classification. Each time series is annotated as flood or no-flood and represents a pixel-wise time series derived from stack of Sentinel-1 IW GRD images that have been pre-processed according to <a href="http://doi.org/10.5281/zenodo.6510223">https://doi.org/10.5281/zenodo.6510223</a>.