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Fig. 2 in A new genus and species of uncertain phylogenetic position within the family Hydrobiidae (Caenogastropoda, Truncatelloidea) discovered in Tunisian springs
Fig. 2. Maximum likelihood inference based on COI dataset. Bootstrap values and BPP's are given when ≥ 70% and 0.90, respectively. Black bars on the right indicate subfamily assignments. Scale bar: expected change per site.
Fig. 3 in A new genus and species of uncertain phylogenetic position within the family Hydrobiidae (Caenogastropoda, Truncatelloidea) discovered in Tunisian springs
Fig. 3. Bullaregia tunisiensis gen. et sp. nov. A. Shell (holotype). B. Operculum. C. Head and penis. D. Coiled body.
Fig. 5 in A new genus and species of uncertain phylogenetic position within the family Hydrobiidae (Caenogastropoda, Truncatelloidea) discovered in Tunisian springs
Fig. 5. Genitalia of Bullaregia tunisiensis gen. et sp. nov. A. Penis (paratype). B. Distal female genitalia. Abbreviations: bc = bursa copulatrix; ov = renal oviduct; SR = seminal receptacle. Scale bar 0.1 mm.
Fig. 5 in Phylogenetic analysis and systematic position of two new species of the ant genus Crematogaster (Hymenoptera, Formicidae) from Southeast Asia
Fig. 5. Phylogeny and distribution of the Crematogaster borneensis-group, C. khmerensisi sp. nov., C. fruhstorferi, C. ssAUS5, C. mjobergi and the C. tetracantha-group. Closed circle indicates C. khmerensis sp. nov., closed square indicates C. pfeifferi sp. nov. It is noted that C. pfeifferi sp. nov. is not represented in the phylogeny.
Fig. 4. A–D in Phylogenetic analysis and systematic position of two new species of the ant genus Crematogaster (Hymenoptera, Formicidae) from Southeast Asia
Fig. 4. A–D. Crematogaster khmerensis sp. nov., worker. A. Body in lateral view. B. Full-face view of head. C. Dorsal view of mesosoma. D. Petiole and postpetiole in dorsal view. — E–H. — Crematogaster pfeifferi sp. nov., worker. E. Body in lateral view. F. Full-face view of head. G. Dorsal view of mesosoma. H. Petiole and postpetiole in dorsal view.
Fig. 3 in Phylogenetic analysis and systematic position of two new species of the ant genus Crematogaster (Hymenoptera, Formicidae) from Southeast Asia
Fig. 3. Posterior estimates of divergence time of 24 taxa on the phylogenetic tree. Blue bars depict the 95% highest posterior density (HPD). Estimations were performed with MCMCTree using the independent rate model.
Fig. 1. Bayesian majority rule consensus tree reconstructed for 90 in Phylogenetic analysis and systematic position of two new species of the ant genus Crematogaster (Hymenoptera, Formicidae) from Southeast Asia
Fig. 1. Bayesian majority rule consensus tree reconstructed for 90 taxa using five genes (ArgK, CAD, LWRh, Top1, Wg) in a MrBayes analysis. Above node numbers indicate posterior probability. Data were partitioned by PartitionFinder v.1.1.1 and analyzed using a best fit model for each gene and codon position, with 10 million generations and a burn-in of 25 %. Area enclosed by dashed lines is enlarged on Fig. 2.
Generalized model-based solutions to false positive error in species detection/non-detection data: DataS5.
<p>Data/code associated with empirical case study (Gray fox relative abundance estimation/prediction) in article "Generalized model-based solutions to false positive error in species detection/non-detection data" [doi pending].</p>
Robust step detection from different waist-worn sensor positions – implications for clinical studies
<p>The dataset contains tri-axial acceleration and gyroscope data (100 Hz sampling) from walks from 19 healthy volunteers, each walking up to three times a parcours of 20 meters with self-selected speed, slow speed or with five soft turns at self-selected speed. Each participant wore 11 time-synchronized sensors during these tests: left/right foot, 5 around waist, non-dominant wrist and upper arm and collar and pocket. In addition to the sensor recordings each 20 meter walk was timed with a stop-watch. </p> <p>Also see: <a href="https://doi.org/10.1159/000511611">https://doi.org/10.1159/000511611</a></p>
Data for "Incidence, clinical course and risk factor for recurrent PCR positivity in discharged COVID-19 patients in Guangzhou, China: a prospective cohort study"
<p>Data for "Incidence, clinical course and risk factor for recurrent PCR positivity in discharged COVID-19 patients in Guangzhou, China: a prospective cohort study"</p>
Investigating the Effect of Positional Variation on Mid-Lactation Mammary Gland Transcriptomics in Mice Fed Either a Low-Fat or High-Fat Diet
<p>Investigating the Effect of Positional Variation on Mid-Lactation Mammary Gland Transcriptomics in Mice Fed Either a Low-Fat or High-Fat Diet.</p>
Accounting for environmental variation in co‐occurrence modelling reveals the importance of positive interactions in root‐associated fungal communities
<p>Understanding the role of interspecific interactions in shaping ecological communities is one of the central goals in community ecology. In fungal communities, measuring interspecific interactions directly is challenging because these communities are composed of large numbers of species, many of which are unculturable. An indirect way of assessing the role of interspecific interactions in determining community structure is to identify the species co-occurrences that are not constrained by the environmental conditions. In this study, we investigated co-occurrences among root-associated fungi, asking whether fungi co-occur more or less strongly than expected based on the environmental conditions and the host plant species examined. For this purpose, we generated molecular data on root-associated fungi of five plant species evenly sampled along an elevational gradient at a high Arctic site. We analysed the data using a joint species distribution modelling approach that allowed us to identify those co-occurrences that could be explained by the environmental conditions and the host plant species, as well as those co-occurrences that remained unexplained and thus more likely reflect interactive associations. Our results indicate that positive interactions play an important role in shaping microbial communities in arctic plant roots. In particular, we found that mycorrhizal fungi are especially prone to positively co-occur with other fungal species. Our results bring new understanding to the structure of arctic interaction networks by suggesting that interactions among root-associated fungi are predominantly positive.</p>
High-quality large curated dataset of protein sequences (1.83 million) and their corresponding Position Specific Scoring Matrices
<p>As part of his master thesis at the Rostlab, which is located at the Technical University of Munich (TUM), Mr. Issar Arab developed the first language model that encodes evolutionary information of proteins explicitly. The pre-training involved the creation of a novel high-quality dataset of protein sequences (around 1.83 million proteins, or ~0.8 Billion amino acids) with their corresponding Position Specific Scoring Matrices (PSSMs). Those matrices reflect the relative frequency of each amino acid at each position in a protein and is derived from evolutionarily related proteins.</p> <p>Mr. Arab makes this work publicly available to help other researchers speed up their work to leverage AI to learn the representation of protein evolutionary information more explicitly. The set of sequences was derived by extracting all PSSMs from the <a href="https://predictprotein.org/">PredictProtein</a> (PP) cache, which were also part o the UniProt Reference Cluster with 50% sequence identity (uniref50 2019_12). The overlap between PP and uniref50 was further filtered to only include high-quality samples, e.g. only multiple sequence alignments with a certain number of aligned sequences were considered. The processing led to a training set of 1.83 Million sequences, a validation set of 879 instances, and a test set of 879 entries. The training data of proteins is reduced to 40% sequence identity, with respect to the validation/test sets, and contains sequences ranging between 18 and 9858 residues in length.</p> <p>Refer to the Jupyter notebook for a detailed description of the files' structure and a Python code snippet to correctly manipulate this data.</p> <p>To access the full original work, please visit the following link: <a href="https://mediatum.ub.tum.de/node?id=1579236">Manuscript</a> <br><br><strong>Note:</strong> The dataset was recently used to fine tune a protein sequence language model (<a href="https://github.com/issararab/PEvoLM">PEvoLM</a>). The work was presented at the CIBCB'23 conference. If you use PEvoLM or this dataset in your work, please cite the following publication:</p> <p>- Issar Arab, <strong>PEvoLM: Protein Sequence Evolutionary Information Language Model</strong>, <em>IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Eindhoven, Netherlands</em>, (2023), pp. 1-8, doi:<a href="https://ieeexplore.ieee.org/document/10264890">10.1109/CIBCB56990.2023.10264890</a></p>
A Kalman Filter Approach to the Fusion of Acceleration, GNSS position and Rotation Sensor Data from Robot Motions
<p><strong>GNSS data:</strong></p> <ul> <li>Instrument: Javad antenna and Septentrio receiver</li> <li>sampling rate: 100 Hz</li> <li>Bandwidth of loop filter: auto adjust</li> <li>Relative positioning </li> <li>Baseline: ultra short with distance of 5 m</li> <li>files in Rinex format: Rover (moving antenna) and Base (stationary antenna), .20G (GLONASS Navigation data), .20N (GPS Navigation data), .20L (Galileo Navigation data), .20O (Observations)</li> </ul> <p><strong>Accelerometer data:</strong></p> <ul> <li>Instrument: EpiSensor and Centaur Digitizer</li> <li>Sampling rate: 250 Hz</li> <li>Unit: counts</li> <li>unfiltered</li> <li>file: XKUK_centaur-6_1233_20200908_114500.seed</li> </ul> <p><strong>Angular rate data:</strong></p> <ul> <li>Instrument: IMU KvH 1750 (includes accelerometer and rotational sensor)</li> <li>Sampling rate: 250 Hz</li> <li>Unit gyro: rad/s</li> <li>Unit accelerometer: g (gravitational acceleration)</li> <li>file: LOGGING_1750_IMU_1308K004_11_57_25_250.csv</li> </ul> <p><strong>Robot Feedback:</strong></p> <ul> <li>Instrument: KUKA model AGILUS KR 6 R900 sixx</li> <li>Sampling rate: 250 Hz</li> <li>Unit translation: m</li> <li>Unit rotation: degree</li> <li>files: kuka_motion_*.txt, 1-4 are consecutive in time.</li> </ul> <p><strong>Experiments:</strong></p> <ul> <li>T: translations, R: rotations, XL, L, S denote the relative amplitudes</li> <li>10 experiments: TLRXL, TLRXL, TLRL, TLRL, TLRS, unfinished TLRS, TLRS, TLRS, TSRS, TSRS (Robot feedback (1,2), angular rate, GNSS data)</li> <li>9 experiments: TLRXL, TLRXL, TLRL, TLRL, TLRS, unfinished TLRS, TLRS, TSRS, TSRS (Robot feedback (3,4), accelerometer data</li> </ul>
Fig. 6 in Nihonella gen. nov., a new troglophilic genus of dwarf spiders from Japan with a discussion on its phylogenetic position within the subfamily Erigoninae (Araneae, Linyphiidae)
Fig. 6. Distribution of Nihonella chika gen. et. sp. nov. in Western Japan. The star refers to the type locality.
Fig. 4 in Nihonella gen. nov., a new troglophilic genus of dwarf spiders from Japan with a discussion on its phylogenetic position within the subfamily Erigoninae (Araneae, Linyphiidae)
Fig. 4. Embolic division and details of the male palp of Nihonella chika gen. et. sp. nov. A. ♂, paratype (NSMT-Ar 20911), embolic division, ventro-retrolateral view. B. Ditto, frontal view. C. Ditto, dorsal view. D. Ditto, ventral view. E. ♂, paratype (NSMT-Ar 20910), palp under SEM microscope, anteroretrolateral view. F. Ditto, detail of the tip of the prolateral tibial apophysis. Abbreviations: see Material and methods.
Fig. 2 in Nihonella gen. nov., a new troglophilic genus of dwarf spiders from Japan with a discussion on its phylogenetic position within the subfamily Erigoninae (Araneae, Linyphiidae)
Fig. 2. Genitalia of Nihonella chika gen. et. sp. nov. A. ♂, holotype (NSMT-Ar 20909), palp, retrolateral view. B. Ditto, prolateral view. C. Ditto, dorsal view. D. Ditto, ventral view. E. ♀, paratype (NSMT- Ar 20910), epigyne, ventral view. F. Ditto, dorsal view. G. Ditto, posterior view. H. Ditto, vulva after being cleared, ventral view. Scale bars: A–D = 0.2 mm; E–H = 0.1 mm.
Fig. 1 in Nihonella gen. nov., a new troglophilic genus of dwarf spiders from Japan with a discussion on its phylogenetic position within the subfamily Erigoninae (Araneae, Linyphiidae)
Fig. 1. Habitus and habitat of Nihonella chika gen. et. sp. nov. A. ♂, holotype (NSMT-Ar 20909), habitus, dorsal view. B. Ditto, ventral view. C. Ditto, lateral view. D. ♀, paratype (NSMT-Ar 20910), habitus, dorsal view. E. Ditto, ventral view. F. Ditto, lateral view. G. Ditto, cephalic region, frontal view. H. Entrance of Anatoyama Cave, type locality of the species. Scale bar = 1 mm.
Fig. 3 in Nihonella gen. nov., a new troglophilic genus of dwarf spiders from Japan with a discussion on its phylogenetic position within the subfamily Erigoninae (Araneae, Linyphiidae)
Fig. 3. Nihonella chika gen. et. sp. nov. A. ♂, holotype (NSMT-Ar 20909), palp, retrolateral view. B. Ditto, prolateral view.C. Ditto, ventral view. D. Ditto, palpal tibia, dorsal view. E. ♀, paratype (NSMT- Ar 20910), epigyne, ventral view. F. Ditto, dorsal view. G. Ditto, vulva, dorsal view. Abbreviations: see Material and methods. Scale bar = 0.1 mm.
Fig. 5 in Nihonella gen. nov., a new troglophilic genus of dwarf spiders from Japan with a discussion on its phylogenetic position within the subfamily Erigoninae (Araneae, Linyphiidae)
Fig. 5. Bayesian inference phylogenetic tree of Erigoninae and other subfamilies of Linyphiidae based on the five concatenated genes discussed in the text. Each color denotes a different subfamily while the Savignia group is indicated in blue. The phylogenetic position of the genus Nihonella gen. nov. is highlighted in red. Numbers at each node denote posterior probability support. Branch lengths are scaled in relation to the number of substitutions per site.
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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.