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Fig. 1 in Feeding of juvenile pirarucu (Arapaima gigas, Arapaimidae) in their natural environment, lago Quatro Bocas, Araguaiana-MT, Brazil
Fig. 1. Partial map showing the Tocantins-Araguaia basin. In prominence, the rio Araguaia. Arrow indicates the lago Quatro Bocas.
Zebra finch song and distance call amplitude measurements: A transmission experiment and observational transects in the natural environment
<p>Birdsong is typically seen as a long-range signal functioning in mate attraction and territory defense. Among birds, the zebra finch is the prime model organism in bioacoustics, yet almost exclusively studied in the lab. In the wild, however, zebra finch song differs strikingly from songbirds commonly studied in the wild as zebra finch males sing most after mating and in the absence of territoriality. Using data from the wild, we here provide an ecological context for a wealth of laboratory studies. By integrating calibrated sound recordings, sound transmission experiments and social ecology of zebra finches in the wild with insights from hearing physiology we show that wild zebra finch song is a very short-range signal with an audible range of about nine meters and that even the louder distance calls do not carry much farther (up to about fourteen meters). These integrated findings provide an ecological context for the interpretation of laboratory studies of this species and indicate that the vocal communication distance of the main laboratory species for avian acoustics contrasts strikingly with songbirds that use their song as a long-range advertisement signal.</p>
Improving wheat yield prediction using secondary traits and high-density phenotyping under heat stressed environments
<p>A primary selection target for wheat (Triticum aestivum) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh. Over five growing seasons, we analyzed normalized difference vegetation index (NDVI) and CT measurements collected in elite breeding lines from the International Maize and Wheat Improvement Center at the Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored several variable reduction and regularization techniques followed by using the combined secondary traits to predict grain yield. Across years, grain yield heritability ranged from 0.30 to 0.72, with variable secondary trait heritability (0.0–0.6), while the correlation between grain yield and secondary traits ranged from−0.5 to 0.5. The prediction accuracy was calculated by a cross-fold validation approach as the correlation between observed and predicted grain yield using univariate and multivariate models. We found that the multivariate models resulted in higher prediction accuracies for grain yield than the univariate models. Stepwise regression performed equal to, or better than, other models in predicting grain yield. When incorporating all secondary traits into the models, we obtained high prediction accuracies (0.58–0.68) across the five growing seasons. Our results show that the optimized phenotypic prediction models can leverage secondary traits to deliver accurate predictions of wheat grain yield, allowing breeding programs to make more robust and rapid selections.</p>
Data from: Phylogenetic relatedness drives protists assembly in marine and terrestrial environments
<p>Aim: Assembly of protists communities is known to be driven mainly by environmental filtering, but the imprint of phylogenetic relatedness is unknown. In this study, we aim to test the degree at which co-occurrences and co-exclusions of protists in different phylogenetic relatedness classes are deviating from random expectation in two ecosystems in order to link them to ecological processes.</p> <p>Location: Global open-oceans and Neotropical rainforest soils</p> <p>Major taxa: Protists</p> <p>Time period: 2009-2013</p> <p>Methods: Protist metabarcoding data originated from two large scale studies. Co-occurrence and co-exclusion networks were constructed using a recent method combining a null distribution model with Spearman's rank correlation coefficients among pairs of OTU. Phylogenetic relatedness was estimated using either global pairwise sequence distance or phylogenetic distance inferred from best maximum-likelihood trees derived from multiple alignments of OTU representative sequences. Significance of observed patterns relating networks and phylogenies were evaluated by distance classes against two null models in which either the tips of the phylogenetic trees or the network edges were randomized.</p> <p>Results: Closely-related protists co-occurred more often than expected by chance in all datasets, but also co-excluded less often than expected by chance in the marine dataset only. Concurrent excess of co-occurrences and co-exclusions were observed at intermediate phylogenetic distances in the marine dataset.</p> <p>Main conclusions: This suggest that environmental filtering and dispersal limitation are the dominant forces driving protists co-occurrences in both environments, while signal of competitive exclusion was only detected in the marine environment. Co-exclusion differences are potentially linked to the individual environments: marine waters are more homogeneous, while the rainforest soils contain a myriad of nutrient rich micro-environment reducing the strength of mutual exclusion.</p>
Dataset for "Radiation environment at the surface and subsurface of the Moon: Model development and validation" publication in Journal of Geophysical Research: Planets
<p>data set used for plots in manuscript "Radiation environment at the surface and subsurface of the Moon: Model development and validation" submitted to GRL</p>
Footstep-Induced Floor Vibration Dataset In Different Deployment Environment
<p>Footstep-induced floor vibration sensing has been used in many smart home applications, such as elderly/patient care, health monitoring. However, impact by the deployment environment, the acquired dataset in the different environments might have different characteristics. We utilize structural vibration sensing to acquire the footstep-induced floor vibration dataset under different humans, wearing different shoes, and in different environments. We also utilize wearable accelerometer sensing to capture the footstep event activity as the ground truth of the vibration dataset.</p> <p>The name format of each variable is as: "People ID_Environment ID_Sensor ID_Lane ID_Shoe ID". In this dataset, we have two human objects, eight environments, four sensors, three lanes, and three different shoes. You can find more details in our published paper.</p> <p>Each variable is a struct, and it contains four files: (1)'vibration': the raw vibration data (2,3,4)'IMU_X/Y/Z': the raw data of wearable accelerometer sensor.</p> <p>Currently, we have two papers published using this dataset [1,2].</p>
Fig. 2 in Crab in amber reveals an early colonization of nonmarine environments during the Cretaceous
Fig. 2. Cretapsaraathanata Luquegen. etsp. nov. from Burmeseamber. (A to G) Detailed views of Holotype LYAM-9. (A) Close-up of antero-ventralcarapace with eyes and eyestalks, antennae, and the excurrent openings beside the palps of the third maxillipeds (mxp3). (B) Close-up of dorsal carapace showing the orbits lacking orbital fissures and the anterolateral marginwith three spines including the outer orbitalspine. (C) Overview of specimenin ventral view with legs, pleon, sternum, mouthparts, eyes, andleft cheliped. (D) Close-upofleft cheliped inventral obliqueview, showingastraight dactylusandpollex, thelatter bearingsmallacuteteeth on occlusalmargin. (E) Ventral view of pereopods 2 to 5, showing all legs of similarsize and slendershape, with small, thin, spaced setae, and an acute, long dactyli. (F) Close-up of themxp3, showing the palp and the fine lining of setae in the occlusal margin, the two hollow openings beside the palp of mxp3 (white arrow), and small granulations in the pterygostome. (G) Close-up of posterior sternites and pleon tucked under the carapace, typical of a eubrachyuran. White arrows in (A) and (F) indicate the excurrent openings. an1, antennula; an2, antenna; ce,corneal eye; es, eyestalk; ex, exopod; is, ischium; me,merus; pa: distal palp of mxp3. Photos by L.X. Figure by J.L.
Fig. 1 in Crab in amber reveals an early colonization of nonmarine environments during the Cretaceous
Fig. 1. Cretapsara athanata Luque gen. et sp. nov., a modern-looking eubrachyuran crab in Burmese amber. (A to D) Holotype LYAM-9. (A) Whole amber sample with crab inclusion in ventral view. (B) Close-up of ventral carapace. (C) Whole amber sample with crab inclusion in dorsal view. (D) Close-up of dorsal carapace. White arrows in (B) and (D) indicate the detached left fifth leg orpereopod. Photos by L.X. Figure by J.L.
Fig. 4. 3D in Crab in amber reveals an early colonization of nonmarine environments during the Cretaceous
Fig. 4. 3D reconstructions of Cretapsara athanata Luque gen. et sp. nov. holotype LYAM-9 and itsgills. (A) Asingle micro-CT slice, coronal section near the base of eyestalks. (B) 3Dmeshextractedfromreconstructedmicro-CT datain VGSTUDIO MAX, remeshedin MeshLab,and visualizedusing Autodesk Maya (see Materialsand Methods); dorsal view showing the position of the gills. (C) Right lateral oblique view. (D) Close-up of (C) showing the right (bottom) and left (top) gills seen from the right side. (E and F) Close-up of the right gills seen from inside (E) and outside (F). Notice the branchial lamellae and the afferent/efferent vessels. Images by E.G.C. Figure by J.L.
Fig. 3. 3D in Crab in amber reveals an early colonization of nonmarine environments during the Cretaceous
Fig. 3. 3D mesh of Cretapsara athanata Luque gen. et sp. nov. holotype LYAM-9. (A to E) 3D mesh extracted from reconstructed micro-CT data in VGSTUDIO MAX, remeshedin MeshLab, andvisualized using Autodesk Maya: (A) dorsal, (B) ventral, (C) right lateral, (D) oblique postero-dorsal, (E) oblique antero-ventral views, showing the claws of equal size and four pairs of slenderlegs similar in shape and size, with P5 slightly smaller than the other legs. (F and G) Details of the dorsal (F) and ventral (G) carapace, showing details of the largeeyes andorbits, small antennae, and asmall, acute outer orbitalspine [(F) thick arrow], two small anterolateral spines (F, thin arrows), aposterolateral marginbearingat leastfoursmallandequidistant tubercles (F, smallarrows), straightposteriormargin, slendercoxae of thepereopods,atypicalheterotreme eubrachyuran sternum (G), andareduced andfolded pleonwith thefirstpleonites dorsally exposed. Leftfifth pereopoddigitallyreattached.bcg, branchiocardiac groove; ca, carpus; cg, cervicalgroove; cx, coxa; da, dactylus; ib, ischiobasis; ma,manus or palm of claw; P1, claws or chelipeds; P2 to P5,pereopods or walking legs 2 to 5; po,pollex or fixed finger cheliped propodus; pr,propodus. Images by E.G.C. Figure by J.L.
REMODEL. WP4. Vision-Based Perception. T4-2. Dynamic environment reconstruction. Data related to a paper presented at 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2021)
<p>Dataset with evaluation results of the paper "New Metrics for Industrial Depth Sensors Evaluation for Precise Robotic Applications", DOI <a href="https://doi.org/10.1109/IROS51168.2021.9636322">10.1109/IROS51168.2021.9636322</a></p>
REMODEL. WP4. Vision-Based Perception. T4-2. Dynamic environment reconstruction. Data related to a paper published on IEEE Access (2022)
<p>Dataset with evaluation results of the paper "Point Cloud Registration With Object-Centric Alignment"; DOI: 10.1109/access.2022.3191352</p>
REMODEL. WP4. Vision-Based Perception. T4-2. Dynamic environment reconstruction. Data related to a paper published on RA-L (2020)
<p>Dataset with evaluation results of the paper "Extrinsic Calibration of an Eye-In-Hand 2D LiDAR Sensor in Unstructured Environments Using ICP" 10.1109/LRA.2020.2965878</p>
UTMInDualSymFi: A Dataset of Dual-band Wi-Fi RSSI Data in Symmetric Indoor Environments
<p>The UTMInDualSymFi database contains a comprehensive source of dual-band Wi-Fi RSSI data.</p> <p>In total, Wi-Fi RSSI data for fingerprinting positioning were collected from 4 residential buildings <br> within Universiti Teknologi Malaysia (UTM) campus:</p> <p> - building 1 = F04 (raw training data + test data + radio maps)<br> - building 2 = CX1 (raw training data + test data + radio maps)<br> - building 3 = F03 (test data only)<br> - building 4 = CY2 (test data only)</p> <p>Buildings 1,3 are similar in structure, symmetric in layout and locations of access points.<br> Buildings 2,4 are similar in structure, symmetric in layout and locations of access points.</p> <p>Each building is multi-floor, with multiple wings at each floor. Wings are labeled as A,B,C.</p> <p>The floor(s)-wing(s) of each building at which data were collected are listed:</p> <p> Building Floor-wing<br> -------- ----------<br> F04 3-A<br> F04 3-B<br> F04 3-C<br> F04 4-A<br> F04 4-B<br> F04 4-C<br> CX1 3-A<br> CX1 3-B<br> CX1 5-A<br> CX1 5-B<br> F03 4-A<br> F03 4-B<br> F03 4-C </p> <p>Data were collected on two laptop devices. Devices are distinguished by wireless network adapters:</p> <p> - Device 1 = Intel<br> - Device 2 = Qualcomm</p> <p>RSSI values range between -50 dBm and -100 dBm<br> RSSI of (+)100 dBm signifies non detection of a certain access point/source</p> <p>Further details are provided in 'README.txt' (included in the data package).</p> <p>Detailed, data elaboration and benchmark performance analysis are reported in the related data descriptor publication: </p> <p>Abdullah, A.; Haris, M.; Aziz, O.A.; Rashid, R.A.; Abdullah, A.S. UTMInDualSymFi: A Dual-Band Wi-Fi Dataset for Fingerprinting Positioning in Symmetric Indoor Environments. <em>Data</em> <strong>2023</strong>, <em>8</em>, 14. https://doi.org/10.3390/data8010014</p> <p><br> </p>
PAsCAL WP6 Pilot 5 Vulnerable Travellers in Connected Transport Environments Video
<p>A summary video documenting and introducing the activities of pilot 5 of the PAsCAL real-world pilots. The part of the pilot which is documented in the video took place within the wider Madrid transport network between May and November 2021. The participants tested the Apertum mobile application (https://apertum.world/) and three groups of potential users were recruited:</p> <ol> <li>Elderly citizens (65+ years), via the non-for-profit organisation Nadiesolo;</li> <li>Paraplegic citizens with permanent mobility constraints and in many cases reduced hand mobility, via the medical centre FLM (Fundación Lesionado Medular);</li> <li>Citizens who were confronted with temporary mobility constraints (e.g., carrying heavy luggage, baby strollers, using crutches or wheelchairs to simulate temporary injuries).</li> </ol>
Fig. 8 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species
Fig. 8 | Total microbial diversity across vertebrate hindguts and within multi- plebodysites of fish. a Hindgutmicrobiotasamplesfrom 569 speciesof vertebrates were rarified to 5000 reads and unique or shared ASVs determined for each class. b The percentage of unique ASVs only found in a given class (not shared in other classes) as compared to the total ASVs within that class. c Rarefaction of cumulative gamma diversity as a function of unique vertebrate species. Included is a single fish species, S. japonicus, sampled over 3 years "black dots" and the unrarefied FMP samples which had detectable bacteria in all four body sites (gill, skin, midgut, and hindgut).d Gammadiversity of 68 fishspeciesacrossfour bodysites.e Percentageof unique ASVs associatedwitha given bodysiteacrossthe 68 fish species.f Rarefaction curve of increasing gamma diversity (inclusive of four body sites) as a function of increasing fish species. ASV amplified sequence variant, 5k 5000.
Fig. 7 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species
Fig. 7 | Microbial source tracking analysis. a Microbial sources of 60 sea water (blue circle) samples taken from 30 unique sampling stations from two timepoints are distributed on a 10 km transect from Torrey Pines beach to Mission Bay. Microbial sources of 108 marine sediment samples (red stars) from San Diego coastalenvironmentincludes 60 paired samples (samelocationsas seawater) from the same 10 km transect along with 58 samples from the various reef habitats near La Jolla. Geographic data presented using ArcGIS. b Sourcetracker2 analysis of likely sources for the four body sites of the fish comparing contributions of beach sand, marine sediment, sea water, and "unknown". Unknown refers to microbes from an unknown source which could include diet and other animals or locations not sampled. c Specific microbial contributions of sea water to the four mucosal body sites and d specific microbial contributions of marine sediment to the four mucosalbodysites b–d: distributionisin medianand interquartilerange.Statistical differences determined using non-parametrictesting Kruskal–Wallistest with 0.05 FDR Benjamini–Hochberg. e Proportion of microbes (distribution is in median and interquartile range) likely originating from the sea water vs. sediment for each unique body site (sea water vs. sediment pairwise comparison for each body site using Mann–Whitney test p <0.05).f Spearmanrho valuesfromcomparisons ofthe ratio of sea water "SW" and marine sediment "SED" against various continuous fish life history metadata variables for each unique body site (Spearman correlation p <0.05). g Comparison of the SW:SED ratios across the habitats from which the fish live. Comparisons performed on each unique body site (Kruskal–Wallis test, p <0.05). *p <0.05, **p <0.01, ***p <0.001, ****p <0.0001, ASV amplified sequence variant ~unique sub-Operational Taxonomic Unit, SD standard deviation, MG midgut, HG hindgut, KW Kruskal–Wallis test statistic "H", IQR inter quartile range, SW sea water.
Fig. 5 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species
Fig. 5 | Biological and life history drivers of mucosal microbiota in diverse sampling of marine fish from Southern California. a Multivariate analysis of biological and life history parameters evaluated using unweighted and weighted normalized UniFrac distances. Statistical significance (PERMANOVA p = 0.001) indicated by yellow blocks (left) and effect size (right). All samples compared together (all) along with individual sample types (gill, skin, midgut, hindgut). b Impact of trophic level on similarity between midgut and hindgut (within a species) (linear model:p p value, mslope,dottedlinesare 95% confidence interval). F-Stat test statistic used in PERMANOVA analysis, all row names in a are metadata column names used in the analysis, MG midgut, HG hindgut, Gen. Weighted UniFrac generalized weighted UniFrac.
Fig. 6 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species
Fig. 6 | Evidence forphylosymbiosis across fishbody sites. Effectof evolutionary distance (low divergence time indicates a short branch length or similar fish species) of all fish compared to a skin unweighted UniFrac distance, b gill generalized weighted UniFrac distance, c hindgut generalized weighted UniFrac distance. Comparisons performed using Mantel test with multiple testing by FDR. Divergence time between fish species calculated using timetree.org. Gen. Weighted UniFrac generalized weighted UniFrac.
Fig. 3 in Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species
Fig. 3 | Alpha diversity and biomass comparisons across ecological and biolo- gical gradients in marine fish. Comparison of microbial diversity a "Chao1", b "Faith's Phylogenetic Diversity", c "Shannon", or d microbial biomassacrossbody site (gill, skin, midgut, and hindgut). Distributions in "red" are median with inter- quartile range. Statistical differences determined using non-parametric testing Kruskal–Wallistest with 0.05 FDR Benjamini–Hochberg. Further testing computed for each unique body site for a variety of biological and ecological metadata categories. Metadata whichis e categorical istestedusing Kruskal–Wallis f whereas numeric metadata tested using Spearmancorrelation. Onlysignificant associations are represented in e (Kruskal–Wallis p <0.05) and f (Spearman p <0.05). KW or KW stat "H" test statistic from Kruskal–Wallis test, MG midgut, HG hindgut, Faith PD Faith's Phylogenetic Diversity metric, GI:TL gastrointestinal length to fish total length "ratio", TL total length of fish.
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.