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Figs 1-4 in Report of the nest of Megachile (Moureapis) benigna (Hymenoptera: Megachilidae) from an urban environment, Rio de Janeiro, Brazil
Figs 1-4. Megachile (Moureapis) benigna Mitchell, 1930: 1, female spawned from the nest (habitus, lateral view); 2, nest collected in the trap-nest; 3, brood cell and round-shaped fragments of leaf forming the cell opercula; 4, different shapes of leaf fragments employed by the female foundress for nest building.
Figs 5, 6 in Report of the nest of Megachile (Moureapis) benigna (Hymenoptera: Megachilidae) from an urban environment, Rio de Janeiro, Brazil
Figs 5, 6. Pollen grains found in the pollen slides analysed from the nest: 5, Cyrtocymura sp. (Asteraceae); 6, Type Fabaceae pollen grain. Pollen grains magnified 40X. Scale bars = 10 µm.
Data from: Nascent transcription reveals regulatory changes in extremophile fishes inhabiting hydrogen sulfide-rich environments
<p>Regulating transcription allows organisms to respond to their environment, both within a single generation (plasticity) and across generations (adaptation). We examined transcriptional differences in gill tissues of fishes in the Poecilia mexicana species complex (family Poeciliidae), which have colonized toxic springs rich in hydrogen sulfide (H2S) in southern Mexico. There are gene expression differences between sulfidic and non-sulfidic populations, yet regulatory mechanisms mediating this gene expression variation remain poorly studied. We combined capped-small RNA sequencing (csRNA-seq), which captures actively transcribed (i.e., nascent) transcripts, and traditional messenger RNA sequencing (mRNA-seq) to examine how variation in transcription, enhancer activity, and associated transcription factor binding sites may facilitate adaptation to extreme environments. csRNA-seq revealed thousands of differentially initiated transcripts between sulfidic and non-sulfidic populations, many of which are involved in H2S detoxification and response. Analyses of transcription factor binding sites in promoter and putative enhancer csRNA-seq peaks identify a suite of transcription factors likely involved in regulating H2S-specific shifts in gene expression, including several key transcription factors known to respond to hypoxia. Our findings uncover a complex interplay of regulatory processes that reflect the divergence of extremophile populations of P. mexicana from their non-sulfidic ancestors and suggest shared responses among evolutionarily independent lineages.</p>
A novel flow chamber for biodegradable alloy assessment in physiologically realistic environments: Supporting Data
<p>Experimental data including Excel analysis and microscopy supporting the publication 'A novel flow chamber for biodegradable alloy assessment in physiologically realistic environments' <a href="https://doi.org/10.1063/1.4821498" target="_blank" rel="noopener">https://doi.org/10.1063/1.4821498</a></p>
Data from: Costs of antibiotic resistance genes depend on host strain and environment and can influence community composition
<p>Antibiotic resistance genes (ARGs) benefit host bacteria in environments containing corresponding antibiotics, but it is less clear how they are maintained in environments where antibiotic selection is weak or sporadic. In particular, few studies have measured the effect of ARGs on host fitness in the absence of direct selection or determined if any costs are fixed or depend on the host strain, perhaps marking some ARG-host combinations as reservoirs that can maintain ARGs in the absence of antibiotic selection. We quantified the fitness effects of six ARGs in 11 diverse <em>Escherichia spp</em>. strains. Three ARGs (blaTEM-116, cat, and dfrA5, encoding resistance to β-lactams, chloramphenicol, and trimethoprim, respectively) imposed an overall cost but all ARGs had an effect in at least one host strain, reflecting a significant strain interaction effect. A simulation predicts these interactions cause the success of ARGs to depend on available host strains, and, to a lesser extent, for successful host strains to depend on the ARGs present in a community. These results indicate the importance of considering ARG effects over different host strains, especially the potential of reservoir strains that allow resistance to persist in the absence of direct selection, in efforts to understand resistance dynamics.</p>
Dataset of chemicals of emerging concern detected in the marine environment in central and northern Patagonia in Chile
<p>This repository encompasses the environmental concentrations of chemicals of emerging concern (CECs), such as pesticides, pharmaceuticals, and industrial chemicals, co-occurring in surface water, porewater, and sediment throughout central and northern Patagonia, Chile.</p>
Mixed salt systems in the built environment - charge balance calculations
<p>A large database of salt mixtures found in the built environment and a charge balance calculations toolkit. Output of the <a href="http://predict.kikirpa.be/">PREDICT</a> project predominantly including measurements of Belgian historic sites collected by KIK-IRPA Monuments Lab.</p> <p>Contents:</p> <ul> <li>Full integrated database and charge balance calculation sheet, including raw ion concentrations and balanced outputs (.xlsx)</li> <li>Sample integrated database (25 mixtures) and charge balance calculation sheet, including raw ion concentrations and balanced outputs (.xlsx)</li> <li>R scripts for charge balance calculations (.R)</li> <li>Full set of raw ion concentrations for the R scripts (.txt)</li> <li>Sample of 25 raw ion concentrations for the R scripts (.txt)</li> <li>Full set of balanced outputs from the R script (.txt)</li> <li>Sample of 25 balanced outputs from the R script (.txt)</li> </ul>
Figure 1 in Hematology and blood biochemistry profile of the freshwater stingray Potamotrygon magdalenae as a tool for population assessment in artificial environments
Figure 1. Different cell types found in peripheral blood of captive adults of Potamotrygon magdalenae.
Figure 2 in Hematology and blood biochemistry profile of the freshwater stingray Potamotrygon magdalenae as a tool for population assessment in artificial environments
Figure 2. Correlation's matrix test among body condition and physiological parameters in captive adults of Potamotrygon magdalenae. RBC:Erythrocyte count; Hb: Hemoglobin; Ht: Hematocrit; Leu: Leukocte count; Thr: Thrombocyte count; He%: Heterophils proportion; Eos%: Eosinophils proportion; Ly%: Lymphocytes proportion; Mo%: Monocytes proportion; Ure: Urea; Tri: Triglycerides; Pp: Plasma proteins; Sp: Serum proteins; Glu: Glucose; Col: Cholesterol. Ellipses show the type of correlation between each variable, slope shows whether the correlation is positive or negative, color intensity shows the correlation's strength, and amplitude of the ellipse corresponds to the data dispersion. The upper diagonal shows the p values for each correlation.
Figure 4 in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
Figure 4: Relative abundance of eukaryotic communities of microinvertebrate external and internal microbiomes: (A) total non-host community (B) metazoan phyla, (C) fungal clades.
Figure 3 in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
Figure 3: Relative abundance of bacterial communities of microinvertebrate external and internal microbiomes: (A) genera of Cyanobacteria, (B) genera of Bacteroidota, (C) families of Proteobacteria, (D) genera of Actinobacteriota.
Figure 2 in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
Figure 2: Diversity of microinvertebrate bacterial external and bacterial internal microbiomes. (A) Shannon's diversity (box plot using Hill Numbers) with a significant difference between microbiomes (P=0.03, GLM) but not microinvertebrates (P=0.14), streams (P=0.22), or mat types (P=0.52). (B) Compositional difference based on Bray Curtis distance matrix visualized with a NMDS ordination, in which microinvertebrate host explained the most variation (R2 =0.14, PERMANOVA). Stars show centroid location of microbiome type, and solid circles show individual microbiomes.
Figure 1 in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
Figure 1: Graphical abstract of methods used to construct external and internal microbiomes of nematodes and tardigrades. For internal microbiomes, microinvertebrates were washed, sequenced, and host sequences subtracted before being averaged for each of the 24 mat replicates and 3 host types. For external microbiomes, unwashed microinvertebrates were sequenced, host sequences subtracted, and averaged for each of the 24 mat replicates and host types. The internal ASV abundances were then subtracted from the corresponding ASVs of unwashed community of the same mat replicate and microinvertebrate type to create the final external microbiome.
SI Figure 4: SEM images of either unwashed (left) or washed (right) E. antarcticus nematodes. A. Unwashed head region with arrows pointing to attached material and possible fungal hyphae. B. Washed head region with arrows pointing to the remaining attached material. C. Unwashed annules with arrows pointing to commonly attached foreign material. D. Washed annules with arrows pointing to remaining attached material. E. Unwashed somatic pore with arrows pointing to the common organic material. F. Washed vulva with an arrow pointing to remaining attached organic material. G. Unwashed cuticle with arrows showing a possible biofilm. H. Washed cuticle showing single attached cells indicated with arrows. I. Unwashed cuticle showing an off-axis line of attached material. J. Washed cuticle showing a similar off-axis line of material (as indicated with arrow) but reduced in quantity compared to the unwashed. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 4: SEM images of either unwashed (left) or washed (right) E. antarcticus nematodes. A. Unwashed head region with arrows pointing to attached material and possible fungal hyphae. B. Washed head region with arrows pointing to the remaining attached material. C. Unwashed annules with arrows pointing to commonly attached foreign material. D. Washed annules with arrows pointing to remaining attached material. E. Unwashed somatic pore with arrows pointing to the common organic material. F. Washed vulva with an arrow pointing to remaining attached organic material. G. Unwashed cuticle with arrows showing a possible biofilm. H. Washed cuticle showing single attached cells indicated with arrows. I. Unwashed cuticle showing an off-axis line of attached material. J. Washed cuticle showing a similar off-axis line of material (as indicated with arrow) but reduced in quantity compared to the unwashed.
SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 3: Compositional difference among eukaryotic microinvertebrate external and internal microbiomes, using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars centroid location of each microbiome type. Communities do not cluster by animal, microbiome type, mat type, or stream.
SI Figure 2: Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 2: Compositional differences among bacterial microinvertebrate external and internal microbiomes as well as mats they were isolated from using Bray Curtis distance matrix visualized with a NMDS ordination. Circles indicate each community and stars show centroids of microbiome types for each animal host. All host microbiomes (internal and external) are distinct from mat communities (P<0.05), but external microbiomes are more similar to mats than internal microbiomes are to mats.
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable.
FIG. 2. — C in Homogeneity of photosynthetic features in canopy-forming macroalgae of the order Fucales from shallow and sheltered environments
FIG. 2. — C:N, C:P, N:P ratios in tissues for each of the studied species. Names are abbreviations from those in Figure 1.
FIG. 1. — A in Homogeneity of photosynthetic features in canopy-forming macroalgae of the order Fucales from shallow and sheltered environments
FIG. 1. — A, photosynthesis/PFD curves for the tested algae, error bars refer to SE; B, lineal fitting of the Photosynthesis/PFD data for the tested algae at the lineal part of the curve. Cystoseira compressa stands for Cystoseira compressa subsp. pustulata (Ercegovic) Verlaque and Cystoseira foeniculacea for Cystoseira foeniculacea f. tenuiramosa (Ercegovic) A.Gómez Garreta, M.C.Barceló, M.A.Ribera & J.Rull Lluch.
Solutions for Reproducibility in Empirical Research: Virtual Machines, Containers, Environment Management Packages, and Cloud Platforms
<p>This image provides a comprehensive overview of various technologies and platforms used to enhance the reproducibility of empirical research. It is divided into several sections:</p> <ol> <li><strong>Virtual Machines (VMs): </strong>the left section of the image illustrates the architecture of VMs with Type 1 and Type 2 hypervisors. <br> - <em>Type 1 Hypervisor </em>runs directly on the hardware, providing high efficiency and performance. Examples include VMware ESXi, <strong>Microsoft Hyper-v</strong>, and Xen Project.<br> - <em>Type 2 Hypervisor</em> runs on an existing operating system, offering flexibility at the cost of some performance. Examples include <strong>Oracle VirtualBox</strong>, VMware Workstation, and Parallels.</li> <li><strong>Containers: </strong>the middle section of the image explains the containerization concept, which shares the host operating system's kernel, making containers more lightweight than VMs. Technologies like <strong>Docker</strong> and <strong>Kubernetes</strong> are shown as popular solutions for container orchestration.</li> <li><strong>Environment Management Packages: </strong>the top right section focuses on tools for managing software dependencies and environments. <strong>renv</strong> (for R) and <strong>Conda</strong> (for Python and other languages) are highlighted as key tools for creating reproducible research environments.</li> <li>Cloud Platforms: the bottom right section features various cloud-based platforms that facilitate reproducible research by providing scalable and shareable computational environments. Platforms include <strong>Google Colab</strong>, <strong>Posit Cloud</strong>, JupyterHub, <strong>Binder</strong>, Nextjournal, OpenShift, and <strong>Code Ocean</strong>.</li> </ol> <p>Together, these solutions provide a robust framework for ensuring that empirical research can be reliably reproduced and validated by others, addressing the challenges of dependency management, environment consistency, and computational resource availability.</p>
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.