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Data for replication of figures in "In situ imaging of a kleptoplastidic ciliate thin layer indicates traditional sampling underestimates oceanic mixotroph biomass", Barua et al. Communications Earth & Environment (2024).
<p>This dataset contains the relevant information needed to reproduce data plots in Figures 1-5 and Supplementary Figures 2-3 of the following manuscript.</p> <p><em><strong>In situ imaging of a kleptoplastidic ciliate thin layer indicates traditional sampling underestimates oceanic mixotroph biomass</strong></em></p> <p>Ranjoy Barua, Lisa Nyman, Buyu Guo, Matthew D. Johnson, Anvita U. Kerkar, Jiarong Hong, Adam T. Greer, John Lehrter, Malcolm McFarland, Bradley Penta, Aditya R. Nayak</p> <p>Communications Earth & Environment (2024).</p>
Fig. 4 in Comparative analysis of peripheral blood reveals transcriptomic adaptations to extreme environments on the Qinghai-Tibetan Plateau in the gray wolf (Canis lupus chanco)
Fig. 4 Reconstructed mitochondrial DNA tree of the worldwide distributed wolves. The numbers at each node are the Bayesian posterior probabilities (right) and ML bootstrap propor- tions (left)
Fig. 3 in Comparative analysis of peripheral blood reveals transcriptomic adaptations to extreme environments on the Qinghai-Tibetan Plateau in the gray wolf (Canis lupus chanco)
Fig. 3 Scatterplot of enriched KEGG pathways for DEGs between the Tibetan and lowland wolves. The enrichment factor is the ratio of the DEG number to the total gene number in the pathway. The dot size and color represent the gene number and the range of the p value respectively
Fig. 1 in Comparative analysis of peripheral blood reveals transcriptomic adaptations to extreme environments on the Qinghai-Tibetan Plateau in the gray wolf (Canis lupus chanco)
Fig. 1 Gene expression profiles of blood in Tibetan and lowland wolves. a Boxplot of the log transformed FPKM expression values across eight wolf blood samples. FPKM: fragments per kilobase of exon per million fragments. The solid horizontal line represents the median, and the box
Data for "Distinct explanations underlie gene-environment interactions in the UK Biobank."
<p>Raw output of scripts run from this repository: https://github.com/arundurvasula/GxE</p>
FIGURE 6 in Ranunculus schmalhausenii (section Batrachium, Ranunculaceae), a neglected water crowfoot endemic to Fennoscandia-a case of rapid hybrid speciation in postglacial environment of North Europe
FIGURE 6. Ranunculus schmalhausenii in the wild in Republic of Karelia, Russia: A—plant in deep water of lake Srednee Kuito (vicinity of town Kalevala), B—whole plant in the shallow of lake Bolshoe Severnoe (vicinity of village Sosnovyi), C, D—floating upper vegetativegenerative part in the shallow of lake Bolshoe Severnoe (vicinity of village Sosnovyi). Photos A.A. Bobrov, (A) 16 July 2012, (B–D) 17 July 2012.
FIGURE 5 in Ranunculus schmalhausenii (section Batrachium, Ranunculaceae), a neglected water crowfoot endemic to Fennoscandia-a case of rapid hybrid speciation in postglacial environment of North Europe
FIGURE 5. Herbarium specimens (H) of Ranunculus schmalhausenii of different morphotypes: A—typical lake form (Russia, Murmansk region, lake Notozero, H 801405), B—typical river form (Finland, Kittilä Lapland, Ylikylä, H 801383), C—form with lobed petals (Finland, Finland proper, Jalassaari, H 801409), D—form with many petaled flowers (Finland, South Häme, lake Norijärvi, H 043172).
FIGURE 4 in Ranunculus schmalhausenii (section Batrachium, Ranunculaceae), a neglected water crowfoot endemic to Fennoscandia-a case of rapid hybrid speciation in postglacial environment of North Europe
FIGURE 4. Phylogenetic relations within Batrachium taxa based on the rpl32-trnL region. Unrooted tree was built in MEGA5 using Maximum Likelihood method (ML). Bootstrap support values from 1000 replicates are shown next to the branches.
FIGURE 8 in Ranunculus schmalhausenii (section Batrachium, Ranunculaceae), a neglected water crowfoot endemic to Fennoscandia-a case of rapid hybrid speciation in postglacial environment of North Europe
FIGURE 8. Individual plant of Ranunculus schmalhausenii (Russia, Republic of Karelia, lake Verknyaya Kuzemka, IBIW) with different types of shoots: A—vegetative, prostrate shoots, B—lower part of generative shoot, C—upper part of generative shoot.
FIGURE 1 in Ranunculus schmalhausenii (section Batrachium, Ranunculaceae), a neglected water crowfoot endemic to Fennoscandia-a case of rapid hybrid speciation in postglacial environment of North Europe
FIGURE 1. Geographical range of Ranunculus schmalhausenii (outlined) with the localities of field investigation and DNA sampling in Finland and North-West of European Russia (dots). Dash line shows preliminary range border due to insufficient data.
FIGURE 3 in Ranunculus schmalhausenii (section Batrachium, Ranunculaceae), a neglected water crowfoot endemic to Fennoscandia-a case of rapid hybrid speciation in postglacial environment of North Europe
FIGURE 3. Phylogenetic relations within Batrachium taxa based on the psbE-petL region. Unrooted tree was built in MEGA5 using Maximum Likelihood method (ML). Bootstrap support values from 1000 replicates are shown next to the branches.
FIGURE 2 in Ranunculus schmalhausenii (section Batrachium, Ranunculaceae), a neglected water crowfoot endemic to Fennoscandia-a case of rapid hybrid speciation in postglacial environment of North Europe
FIGURE 2. NeighborNet analysis of interspecific ITS variability within the Batrachium section. The concentration of Ranunculus schmalhausenii samples are marked by small red rectangle; big red rectangle—close up view showing the genetic relations among the samples of R. schmalhausenii (samples marked red) and R. penicillatus (samples given in italic). Bootstrap support values from 1000 replicates are shown next to the branches. Each of the five R. trichophyllus ribotypes is marked by black rectangle. Genetic groups are marked A–F.
FIGURE 7 in Ranunculus schmalhausenii (section Batrachium, Ranunculaceae), a neglected water crowfoot endemic to Fennoscandia-a case of rapid hybrid speciation in postglacial environment of North Europe
FIGURE 7. Ranunculus peltatus: A—typical form (C Poland, Świętokrzyskie prov., village Aleksandrów, reservoir on river Korzeniówka, KRA 327607), B—characteristic stand (W Poland, Lubuskie prov., oxbow of river Odra near Łomy, photo J. Zalewska-Gałosz, 8 June 2012).
FIGURES 18–25 in Description of Stauroneis saprophila sp. nov. (Bacillariophyta), a new diatom species from anthropogenic environment
FIGURES 18–25. SEM images of Stauroneis saprophila. 18–20 External view of an entire valve. 21. Internal valve view of entire valve. 22. External view of valve apex, with visible girdle band perforations. 23. External view of central area. 24. External view of valve apex with distal raphe end. 25. Internal view of valve apex with clearly visible pseudoseptum. Scale bars: Figs 18–21, 25 = 10 μm, Figs 22–24 = 5 μm.
FIGURES 1–2. Sampling site. 1 in Description of Stauroneis saprophila sp. nov. (Bacillariophyta), a new diatom species from anthropogenic environment
FIGURES 1–2. Sampling site. 1. Place of water outflow from storm sewer. 2. The end section of the water outflow finished by puddle.
AquaXAI: Visual and Textual Dataset for XAI in Aquatic Environments
<p>This Dataset is for the publication: "Interactive Simulator Framework for XAI Applications in Aquatic Environments"<br><br>AquaXAI is a synthetic dataset aiming to explain boat skippers' actions in aquatic environments, using Unity Simulator.</p> <p>The dataset consists of:</p> <p>Image: RGB + Semantic Segmentation + Instance Segmentation + Bounding Box</p> <p>Textual Explanations: Action + Explanation</p>
STEM CO-EDUCATIONAL ENVIRONMENTS COMBINING EDUCATIONAL ROBOTICS AND COMPUTATIONAL THINKING
<h1><span>AMBIENTES COEDUCATIVOS STEM QUE COMBINAN ROBÓTICA EDUCATIVA Y PENSAMIENTO COMPUTACIONAL </span></h1> <p><span> </span></p> <p><strong><span>STEM CO-EDUCATIONAL ENVIRONMENTS COMBINING EDUCATIONAL ROBOTICS AND COMPUTATIONAL THINKING</span></strong></p> <p> </p> <p> </p> <p> </p> <p><span>Resumen en español </span></p> <p><span>El presente artículo de investigación se enfoca en abordar las desigualdades de género en el ámbito educativo, especialmente en las disciplinas STEM, mediante el uso de la robótica educativa y el pensamiento computacional. Para ello se realiza una revisión sistemática de literatura, con el objetivo de analizar tendencias, desafíos y oportunidades en la implementación de estas estrategias en Colombia y su impacto en el cierre de brechas de género. Aunque no se encontró una revisión que sintetice la evidencia de estos temas de manera conjunta, el estudio actual busca llenar ese vacío y contribuir a intervenciones educativas futuras más equitativas.</span></p> <p><span>Palabras clave (en español): </span><span>Educación STEM, Robótica educativa, Pensamiento computacional, Brechas de género, Vocaciones científicas</span><span>.</span><span> </span></p> <p>Resumen en inglés</p> <p><span>This research article focuses on addressing gender inequalities in education, especially in STEM disciplines, through the use of educational robotics and computational thinking. To this end, a systematic literature review is conducted, with the aim of analyzing trends, challenges and opportunities in the implementation of these strategies in Colombia and their impact on closing gender gaps. Although no review was found that synthesizes the evidence on these topics together, the current study seeks to fill this gap and contribute to more equitable future educational interventions.</span></p> <p><span>Palabras clave (en inglés): STEM education, educational robotics, Computational thinking, Gender gaps, Scientific vocations. </span></p>
Safe trajectories from local information for coverage control in non-convex environments
<h2>Description</h2> <p>This dataset contains 3-channels grid-based representations of local information individually retrieved by robots in a team, tasked with a coverage control operation. Data collection was performed running 50 episodes of a coverage control mission with a team of 16 robots controlled by a theoretically proven safe expert controller.</p> <h3>Features</h3> <p>Features encode local information in a 3-channels <em>64 x 64</em> image, corresponding to the discretized sensing region of the robot. The first channel encodes the local likelihood density, the second one the position of team-mates, and the third channel contains the position of obstacles and boundaries. </p> <p><em>imgs{i}.npy</em> files contain data collected over each episode in the form of a [<em>S, N, C, W,</em> W] <em>numpy </em>array, where S is the number of steps of that episode, N is the number of robots, C = 3 is the number of channels, and W = 64 is the size of the image. </p> <h3>Labels</h3> <p>Labels contain the 2D velocity calculated by the expert controller, which is theoretically proven to guarantee collision avoidance. </p> <p><em>vels{i}.npy </em>files contain data collected over each episode in the form of a [<em>S, N, 2</em>] <em>numpy</em> array, associated to the corresponding feature.</p> <p> </p> <h2>Training and Testing</h2> <p>Code for training and testing a CNN-based model mapping local information to 2D velocity is available at <a href="https://github.com/ARSControl/cnn_coverage.git">https://github.com/ARSControl/cnn_coverage.git</a>.</p> <p> </p> <h2>Contact Information</h2> <p>If you are interested in any further information, please contact <a href="mailto:mattia.catellani@unimore.it">mattia.catellani@unimore.it</a>.</p> <p> </p>
Multi-UAV Uniform Sweep Coverage in Unknown Environments: A Mergeable Nervous System (MNS)-Based Random Exploration
<p><strong>Abstract:</strong><br>This paper investigates the problem of multi-UAV uniform sweep coverage, where a homogeneous swarm of UAVs must collectively and evenly visit every portion of an unknown environment for a sampling task without having access to their own location and orientation. Random walk-based exploration strategies are practical for such a coverage scenario as they do not rely on localization and are easily implementable in robot swarms. We demonstrate that the Mergeable Nervous System (MNS) framework, which enables a robot swarm to self-organize into a hierarchical ad-hoc communication network using local communication, is a promising control approach for random exploration in unknown environments by UAV swarms. To this end, we propose an MNS-based random walk approach where UAVs self-organize into a line formation using the MNS framework and then follow a random walk strategy to cover the environment while maintaining the formation. Through simulations, we test the efficiency of our approach against several decentralized random walk-based strategies as benchmarks. Our results show that the MNS-based random walk outperforms the benchmarks in terms of the time required to achieve full coverage and the coverage uniformity at that time, assessed across both the entire environment and within local regions.</p>
Rapid Flood Simulation and Source Area Identification in Urban Environments via Interpretable Deep Learning
<p>Here is the data and original code for the paper titled <em>Rapid Flood Simulation and Source Area Identification in Urban Environments via Interpretable Deep Learning</em>. If you have any questions, please contact <a rel="noopener">202331470015@mail.bnu.edu.cn</a>.</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.