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Cover and frequency of biological soil crust community types, moss species, vascular plants, and abiotic land surface features, on gypsum & non-gypsum soils from the Chihuahuan and Mojave Deserts in 2023
This dataset contains raw and calculated percent cover and frequency data for biological soil crust (hereafter biocrust) functional groups, vascular plant functional groups, and abiotic land surface features on and off gypsum soils in the northern Chihuahuan and eastern Mojave Deserts. Abundance data were obtained from 20 study sites total, 10 located on soils derived from gypsum parent material and 10 located on soils derived from non-gypsum parent materials. Sites were grouped into 10 pairs, in which every gypsum site was partnered with a non-gypsum site located in the same region. Apart from soil type, partnered-site characteristics (topography, climate, elevation, slope, aspect, and presence of biocrusts) were held relatively constant. At each site, cover and frequency assessments were made using the line-point intercept method (LPI) and frequency quadrats (1.0 m^2), respectively. Biocrust functional groups included the following crusts: lichen, moss, incipient algal, light algal, dark algal, unknown photosynthetic crust, and vagrant cyanobacteria. Vascular plant categories included: perennial forbs, perennial graminoids, annual forbs, annual graminoids, subshrub, shrub, Yucca, and cacti. Abiotic land surface features included: woody litter, herbaceous litter, bare soil, rock, bedrock, and animal feces. Moss crusts identified within cover and frequency analyses were sampled, and classified to species level via microscopy. The resulting percent cover and frequency data was used to understand differences in biocrust and moss species abundance and diversity on and off gypsum soils; furthermore, how biocrust and moss species abundance was associated with the measured environmental variables. Soil physical and chemical data from this study can be accessed at knb-lter-jrn.210616002. This study and dataset are complete.
Long-term record of wetted area, substrate type, and plant features in Sycamore Creek, Arizona, USA (2010-2020)
The primary objective of this project is to understand how long-term climate variability and change influence the structure and function of desert streams via effects on hydrologic disturbance regimes. Climate and hydrology are intimately linked in arid landscapes; for this reason, desert streams are particularly well suited for both observing and understanding the consequences of climate variability and directional change. Researchers try to (1) determine how climate variability and change over multiple years influence stream biogeomorphic structure (i.e., prevalence and persistence of wetland and gravel-bed ecosystem states) via their influence on factors that control vegetation biomass, and (2) compare interannual variability in within-year successional patterns in ecosystem processes and community structure of primary producers and consumers of two contrasting reach types (wetland and gravel-bed stream reaches). This dataset was collected to understand changes of biota patch cover in different sites and reaches of Sycamore Creek and the transition of different algal and plant communities in post-flood succession.
Fig. 8. General Charipinae features. A in Revision of the types of species of Alloxysta described by Cameron and Fergusson (Hymenoptera: Figitidae: Charipinae) and deposited in the Natural History Museum (London), including a key to the fauna of Great Britain
Fig. 8. General Charipinae features. A. Closed radial cell (Alloxysta brevis). B. Partially open radial cell (A. macrophadna). C. Open radial cell (A. medinae). D. Pronotal carinae absent (A. brevis). E. Pronotal carinae present (A. citripes). F. Propodeal carinae absent (A. victrix). G. Propodeal carinae present (A. castanea).
On the nature of mixed-type features in materials datasets
<p>We provide four crystalline materials datasets that contain both numerical and categorical features of materials.</p> <ol> <li>The Lattice dataset [1,2] contains 1,439 binary AB body-centered cubic crystals, as described by 12 numerical and 9 categorical variables. The lattice constant is considered as the physical property of interest.</li> <li>The TC dataset [2,3] contains 101 binary alloys of transition and rare earth metals, as described by 15 numerical and 17 categorical features. The Curie temperature (TC) is considered as the property of interest.</li> <li>The Octet binary materials dataset [4] contains 82 materials, as described by 11 numerical and 2 categorical features. The difference in LDA energy between RS and ZB (∆E = E(RS) − E(ZB)) in eV is considered as the physical property of interest.</li> <li>The Fm3m dataset [5] contains 239 binary compounds collected from the Materials Project, as described by 12 numerical and 17 categorical variables. The formation of energy is considered as the physical property of interest.</li> </ol> <p>References:</p> <p>[1] K. Takahashi, L. Takahashi, J. D. Baran, and Y. Tanaka, "Descriptors for predicting the lattice constant of body centered cubic crystal", The Journal of chemical physics 146, 204104 (2017).<br> [2] D.-N. Nguyen, T.-L. Pham, V.-C. Nguyen, T.-D. Ho, T. Tran, K. Takahashi, and H.-C. Dam, "Committee machine that votes for similarity between materials", IUCrJ 5, 830-840 (2018).<br> [3] Y. Xu, M. Yamazaki, and P. Villars, "Inorganic materials database for exploring the nature of material", Japanese Journal of Applied Physics 50, 11RH02 (2011).<br> [4] L. M. Ghiringhelli, J. Vybiral, S. V. Levchenko, C. Draxl, and M. Scheer, "Big data of materials science: critical role of the descriptor", Physical review letters 114, 105503 (2015).<br> [5] A. Jain, S. P. Ong, G. Hautier, W. Chen, W. D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, et al., "Commentary: The materials project: A materials genome approach to accelerating materials innovation", Apl Materials 1, 011002 (2013).</p>
Classification of Gene Mutations and Cancer-Types with BioBombe Compressed Gene Expression Features
<p>BioBombe analysis applied to gene expression data from The Cancer Genome Atlas (TCGA) PanCanAtlas.</p> <p>Then, the compressed features learned through the serial compression are fed into several machine learning algorithms.</p> <p>The algorithms are used to predict mutation status (for the top 50 most mutated genes in TCGA) and cancer-type using the compressed gene expression features.</p> <p>Method and results described in https://github.com/greenelab/BioBombe. We use the classification approach outlined in https://github.com/greenelab/pancancer</p>
Figure 5 in Ecological and faunistic features of caddisflies (Insecta: Trichoptera) in different types of habitats in the Dinaric karst area (Central Croatia)
Figure 5. Maximum likelihood (ML) phylogram based on 658 bp long fragment of the mt COI DNA barcode region showing the relationships between species of the genus Setodess. Numbers above the branches represent bootstrap support (BS) for Neighbor-Joining (NJ) and ML analysis (NJ/ML). BS values less than 60 are not shown. Specimen ID from sequences obtained in this study are written with bold letters.
Figure 3 in Ecological and faunistic features of caddisflies (Insecta: Trichoptera) in different types of habitats in the Dinaric karst area (Central Croatia)
Figure 3. NMDS similarity analysis of caddisflies fauna at the study area (sampling sites correspond to the list in Tab. 1).
Figure 2 in Ecological and faunistic features of caddisflies (Insecta: Trichoptera) in different types of habitats in the Dinaric karst area (Central Croatia)
Figure 2. Dobra - spring (D1), Dobra - upper Dobra (D2), Dobra - canyon (D4), Kamačnik - spring (K1), Zagorska Mrežnica - spring (ZM), Sabljaci - reservoir (SR).
Figure 6 in Ecological and faunistic features of caddisflies (Insecta: Trichoptera) in different types of habitats in the Dinaric karst area (Central Croatia)
Figure 6. Dobra - spring (D1). A) before channelization (2010), B) after channelization (2020).
Figure 1 in Ecological and faunistic features of caddisflies (Insecta: Trichoptera) in different types of habitats in the Dinaric karst area (Central Croatia)
Figure 1. Map of the study area with sampling sites which correspond to the list in Tab. 1.
Predictive modeling for clinical features associated with Neurofibromatosis Type 1
<p>Objective: Perform a longitudinal analysis of clinical features associated with Neurofibromatosis Type 1 (NF1) based on demographic and clinical characteristics, and to apply a machine learning strategy to determine feasibility of developing exploratory predictive models of optic pathway glioma (OPG) and attention-deficit/hyperactivity disorder (ADHD) in a pediatric NF1 cohort.</p> <p>Methods: Using NF1 as a model system, we perform retrospective data analyses utilizing a manually-curated NF1 clinical registry and electronic health record (EHR) information, and develop machine-learning models. Data for 798 individuals were available, with 578 comprising the pediatric cohort used for analysis.</p> <p>Results: Males and females were evenly represented in the cohort. White children were more likely to develop OPG (OR: 2.11, 95%CI: 1.11-4.00, p=0.02) relative to their non-white peers. Median age at diagnosis of OPG was 6.5 years (1.7-17.0), irrespective of sex. Males were more likely than females to have a diagnosis of ADHD (OR: 1.90, 95%CI: 1.33-2.70, p<0.001), and earlier diagnosis in males relative to females was observed. The gradient boosting classification model predicted diagnosis of ADHD with an AUROC of 0.74, and predicted diagnosis of OPG with an AUROC of 0.82.</p> <p>Conclusions: Using readily available clinical and EHR data, we successfully recapitulated several important and clinically-relevant patterns in NF1 semiology specifically based on demographic and clinical characteristics. Naïve machine learning techniques can be potentially used to develop and validate predictive phenotype complexes applicable to risk stratification and disease management in NF1.</p>
FIGURE 6 in Resolution of the types, diagnostic features, and distribution of two easily confused Sand Lizards, Pedioplanis laticeps (Smith, 1845) and P. burchelli (Duméril & Bibron, 1839) (Squamata: Lacertidae)
FIGURE 6. Life pictures of the two newly collected individuals of P. laticeps from Aussenkehr/Namibia: (A) adult female ZMB 82782, (B) adult male ZMB 82783.
FIGURE 5. Holotype BMNH 1946.8.7.82 in Resolution of the types, diagnostic features, and distribution of two easily confused Sand Lizards, Pedioplanis laticeps (Smith, 1845) and P. burchelli (Duméril & Bibron, 1839) (Squamata: Lacertidae)
FIGURE 5. Holotype BMNH 1946.8.7.82 (previously 65.5.4.39) of Eremias burchelli Duméril & Bibron, 1839: (A) original drawing of the whole individual modified from Plate 45: 1 (Smith 1845); (B) dorsal view of the museum voucher; (C) ventral view; (D) dorsal head; (E) original drawing of the dorsal head modified from Plate 48: 4 (Smith 1845); (F) ventral head; (G) lateral head left; (H) original drawing of the lateral head modified from Plate 48: 4a (Smith 1845); (I) lateral head right. Scale units 1 mm.
FIGURE 4. Holotype BMNH 1946.8.7.56 in Resolution of the types, diagnostic features, and distribution of two easily confused Sand Lizards, Pedioplanis laticeps (Smith, 1845) and P. burchelli (Duméril & Bibron, 1839) (Squamata: Lacertidae)
FIGURE 4. Holotype BMNH 1946.8.7.56 (previously 65.5.4.55) of Eremias laticeps Smith, 1845: (A) original drawing of the whole individual modified from Plate 46: 1 (Smith 1845); (B) dorsal view of the museum voucher; (C) ventral view; (D) dorsal head; (E) original drawing of the dorsal head modified from Plate 48: 3 (Smith 1845); (F) ventral head; (G) lateral head left; (H) original drawing of the lateral head modified from Plate 48: 3a (Smith 1845); (I) lateral head right. Scale units 1 mm.
FIGURE 2 in Resolution of the types, diagnostic features, and distribution of two easily confused Sand Lizards, Pedioplanis laticeps (Smith, 1845) and P. burchelli (Duméril & Bibron, 1839) (Squamata: Lacertidae)
FIGURE 2. Distribution of P. laticeps compiled from the museum collections of the Museum for Naturkunde Berlin, (Germany), Naturhistorisches Museum Wien (Austria), Ditsong National Museum of Natural History Pretoria, Port Elizabeth Museum, IZIKO South African Museum Cape Town (all South Africa), Natural History Museum London (Great Britain) and the National Museum of Namibia in Windhoek (black dots) including the new record from near Aussenkehr (star). Points are underlain with the altitude level (downloaded from www.worldclim.org) ascending from light grey (sea level) to dark grey (6000 m). The Namibian localities are numbered from 1 through 6 (1: Farm Tafelkop; 2: Farm Louwshoop 330; 3: Klein Karas; 4: Ortmansbaum; 5: Farm Lützelhöhe; 6: Warmbad), the? represents the doubtful record from Angra Pequena/Lüderitz. White spots represent the centre of quarter-degree grids as published by Bates et al. (2014).
FIGURE 3. Holotype BMNH 1946.8.7.86 in Resolution of the types, diagnostic features, and distribution of two easily confused Sand Lizards, Pedioplanis laticeps (Smith, 1845) and P. burchelli (Duméril & Bibron, 1839) (Squamata: Lacertidae)
FIGURE 3. Holotype BMNH 1946.8.7.86 (previously IV.42.a) of Lacerta capensis Smith, 1838: (A) original drawing of the whole individual modified from Plate 45: 2 (Smith 1845); (B) dorsal view of the museum voucher; (C) ventral view; (D) dorsal head; (E) original drawing of the dorsal head modified from Plate 48: 7 (Smith 1845); (F) ventral head; (G) lateral head left; (H) original drawing of the lateral head modified from Plate 48: 7a (Smith 1845); (I) lateral head right. Scale units 1 mm.
FIGURE 1 in Resolution of the types, diagnostic features, and distribution of two easily confused Sand Lizards, Pedioplanis laticeps (Smith, 1845) and P. burchelli (Duméril & Bibron, 1839) (Squamata: Lacertidae)
FIGURE 1. Example of the counting method for enlarged scales arranged in rows (labials, collar, chin shields, ventral scales) using the upper and lower labials. All labials marked with a black dot were counted while the ones marked with X were not. This means that 9 upper labials and 6 lower labials were counted on this specimen.
FIGURES 45–53. Cocconeis sawensis, type material from Iraq. 45–50 SEM. 51–53 TEM. 45, 46 in Additional morphological features of the epiphytic diatom Cocconeis sawensis Al-Handal & Riaux-Gobin (Cocconeidaceae, Bacillariophyta) from a coastal lagoon, Southern Brazil
FIGURES 45–53. Cocconeis sawensis, type material from Iraq. 45–50 SEM. 51–53 TEM. 45, 46. External view of the valve raphe. 47–49. Internal view of the sternum valve. 50. External view of the sternum valve. 51. General view of the sternum valve. 52. Areolae with broken hymens. 53. Detail of the fimbriae of sternum valve valvocopula. Scale bars = 2 μm (Figs 45–47, 50, 51), 5 μm (Figs 48, 49), 0.5 μm (Fig. 52), 1 μm (Fig. 53).
FIGURE 1 in New records of Neopetrosia carbonaria (Lamarck, 1814) from the Brazilian coast reveal new morphological features and spicule types
FIGURE 1. Collection sites of Neopetrosia carbonaria in the Northeastern Brazilian coast (CE—Ceará State; RN—Rio Grande do Norte State; PB—Paraíba State; PE—Pernambuco State; AL—Alagoas State and BA—Bahia State).
FIGURE 3 in New records of Neopetrosia carbonaria (Lamarck, 1814) from the Brazilian coast reveal new morphological features and spicule types
FIGURE 3. Anatomical characters of Neopetrosia carbonaria. (A–B) Tangential view of the ectosomal skeleton, (C) Cross section of the skeleton, showing the subdermal layer and the inner choanosome. Scale bars: (A–B) 500 μm, (C) 1 mm.
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.