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FIGURE 2 in Contribution to the knowledge of the oribatid mite genus Microzetes (Acari, Oribatida, Microzetidae)
FIGURE 2: Microzetes (Microzetes) samarensis n. sp.: A – lateral view, right side (gnathosoma and legs not illustrated); B – subcapitulum, ventral view; C – palptarsus, right, antiaxial view; D – anterior part of chelicera, left, paraxial view. Scale bar (A) 50 µm, scale bar (B – D) 20 µm.
FIGURE 5 in New data on the Philippine oribatid mite fauna, with a contribution to knowledge of the genus Drymobatoides (Acari, Oribatida, Mochlozetidae)
FIGURE 5: Drymobatoides boronganensis n. sp.: A – leg I, right, antiaxial view (solenidia σ and '1 broken); B – leg I, without trochanter, right, antiaxial view; C – trochanter, femur and genu of leg III, left, antiaxial view; D – trochanter, femur and genu of leg IV, left, antiaxial view. Scale bar 50 µm.
FIGURE 4 in New data on the Philippine oribatid mite fauna, with a contribution to knowledge of the genus Drymobatoides (Acari, Oribatida, Mochlozetidae)
FIGURE 4: Drymobatoides boronganensis n. sp.: A – subcapitulum, ventral view; B – palptarsus, right, antiaxial view; C – medio-anterior part of chelicera, left, paraxial view. Scale bars (A, C) 50 µm; scale bar (B) 20 µm.
FIGURE 2 in New data on the Philippine oribatid mite fauna, with a contribution to knowledge of the genus Drymobatoides (Acari, Oribatida, Mochlozetidae)
FIGURE 2: Drymobatoides boronganensis n. sp.: ventral view (legs not illustrated). Scale bar 100 µm.
Figure 6 in A contribution to the knowledge of Quadraseta brasiliensis Goff and Gettinger, 1989 (Trombidiformes: Trombiculidae), with description of the deutonymph instar
Figure 6 Geographical distribution ofQuadraseta brasiliensis.
Figure 8 in First record of the taxon Echinopsyllus (Copepoda, Harpacticoida, Ancorabolidae) from the deep sea of Campos Basin, Brazil, with the description of three new species and their contribution to phylogenetic analysis
Figure 8. Echinopsyllus nogueirae sp. nov., female. A, A1, ventral view; B, mxp. Scale bar = 50 Mm.
Figure 9 in First record of the taxon Echinopsyllus (Copepoda, Harpacticoida, Ancorabolidae) from the deep sea of Campos Basin, Brazil, with the description of three new species and their contribution to phylogenetic analysis
Figure 9. Echinopsyllus nogueirae sp. nov., female. A, A2; B, P5 with P6. Scale bar = 50 Mm.
Figure 1 in First record of the taxon Echinopsyllus (Copepoda, Harpacticoida, Ancorabolidae) from the deep sea of Campos Basin, Brazil, with the description of three new species and their contribution to phylogenetic analysis
Figure 1. Map of Campos Basin showing the sampling stations (modified from Botelho et al. 2007).
Figure 11 in First record of the taxon Echinopsyllus (Copepoda, Harpacticoida, Ancorabolidae) from the deep sea of Campos Basin, Brazil, with the description of three new species and their contribution to phylogenetic analysis
Figure 11. Echinopsyllus nogueirae sp. nov., female. A, P3; B, P4. Scale bar = 50 Mm.
Figure 9 in Revisiting the contribution of larval characters to an analysis of phylogenetic relationships of basal anurans
Figure 9. Anterior view of mouths of (a) Pipa myersi and (b) Rana palmipes. Not to scale.
Cleaned data from Supplementary Table S1 from "Temperature-Dependent Estimation of Gibbs Energies Using an Updated Group-Contribution Method"
<p>This data is derived from <a href="https://doi.org/10.5281/zenodo.5277805">https://doi.org/10.5281/zenodo.5277805</a>. As of 2021-09-08, <a href="https://doi.org/10.5281/zenodo.5277805">https://doi.org/10.5281/zenodo.5277805</a> contained an Excel file with md5:8907e7444f49d02d58fdef9fb2a1089a and filename "TableS1.xlsx"; the file was licensed under <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a>.</p> <p>In "TableS1.xlsx" there were, among others, worksheets with the names "Table S1. TECRDB Keqs" and "Table S2. TECRDB ΔrH data".</p> <p>Worksheet "Table S1. TECRDB Keqs" was exported as a csv with filename "TableS1_Keq.csv". A column "id" was added with a persistent identifier created in w3id.org.</p> <p>Worksheet "Table S2. TECRDB ΔrH data"" was exported as a csv with filename "TableS1_deltaH.csv". A column "id" was added with a persistent identifier created in w3id.org.</p> <p>No other corrections were made.</p>
Measuring the contribution of evolution to community trait structure in freshwater zooplankton
<p>There are currently few predictions about when evolutionary processes are likely to play an important role in structuring community features.Determining predictors that indicate when evolution is expected to impact ecological processes in natural landscapes can help researchers identify eco-evolutionary 'hotspots', where eco-evolutionary interactions are more likely to occur. Using data collected from a survey in freshwater cladoceran communities, landscape population genetic data, and phenotypic trait data measured in a common garden, we applied a Bayesian linear model to assess whether the impact of local trait evolution in the keystone species <i>Daphnia magna</i> on cladoceran community trait values could be predicted by population genetic properties (within-population genetic diversity, genetic distance among populations), ecological properties (Simpson's diversity, phenotypic divergence), or environmental divergence. We found that the impact of local trait evolution varied among communities. Moreover, community diversity and phenotypic divergence were found to be better predictors of the contribution of evolution to community trait values than environmental features or genetic properties of the evolving species. Our results thus indicate the importance of ecological context for the impact of evolution on community features. Our study also demonstrates one way to detect signatures of eco-evolutionary interactions in communities inhabiting heterogeneous landscapes using survey data of contemporary ecological and evolutionary structure.</p>
Aboveground net primary productivity in regenerating seasonally dry tropical forest: contributions of rainfall, forest age, and soil
<p>Identifying factors controlling forest productivity is critical to understanding forest-climate change feedbacks, modeling vegetation dynamics, and carbon finance schemes. However, little research has focused on productivity in regenerating tropical forest which are expanding in their fraction of global area have an order of magnitude larger carbon uptake rates relative to older forest.</p> <p>We examined aboveground net primary productivity (ANPP) and its components (wood production and litterfall) over ten years in forest plots that vary in successional age, soil characteristics, and species composition using band dendrometers and litterfall traps in regenerating seasonally dry tropical forests in northwestern Costa Rica.</p> <p>We show that the components of ANPP are differentially driven by age and annual rainfall and that local soil variation is important. Total ANPP was explained by a combination of age, annual rainfall, and soil variation. Wood production comprised 35% of ANPP on average across sites and years, and was explained by annual rainfall but not forest age. Conversely, litterfall increased with forest age and soil fertility yet was not affected by annual rainfall. In this region, edaphic variability is highly correlated with plant community composition. Thus, variation in ecosystem processes explained by soil may also be partially explained by species composition.</p> <p>These results suggest that future changes in annual rainfall can alter the secondary forest carbon sink, but that this effect will be buffered by the litterfall flux which varies little among years. In determining the long-term strength of the secondary forest carbon sink, both rainfall and forest age will be critical variables to track. We also conclude that a detailed understanding of local site variation in soils and plant communities may be required to accurately predict the impact of changing rainfall on forest carbon uptake.</p> <p>Synthesis We show that in seasonally dry tropical forests, annual rainfall has a positive relationship with the growth of aboveground woody tissues of trees and that droughts lead to significant reductions in aboveground productivity. These results provide evidence for climate change – carbon cycle feedbacks in the seasonal tropics and highlight the value of longitudinal data on forest regeneration.</p>
Large contribution of recent photosynthate to soil respiration in tropical dipterocarp forest revealed by girdling
<b>Description: </b><p>The research site is one of the existing intensive carbon plots (Tower Plot) at the SAFE Project Experimental area. The area where the plot is located will be converted into oil palm plantation during 2015-2017 (for commercial purposes, not for research). The overarching aim of the project is to assess how the termination of the transport of sugars and defoliation alter forest ecosystem functioning and structure.The aim of the project is:<br>1. To quantify the contribution of photosynthate supply to soil respiration: via the contribution of roots and soil microbial communities utilising root-derived carbon.<br>2. To assess whether there is a relationship between root respiration and tree species.<br>To address these aims, we girdled trees in one half of the plot (0.5 ha), leaving the other half (0.5 ha) as a control. In girdling, a strip of bark (including cambium and phloem) was removed from around the trunk, with the aim of stopping the transport of sugars from the foliage into the roots and soil. The transport of sugars stop immediately, allowing us to quantify their role in the root and soil processes. The girdled trees will gradually defoliate and die due to the carbon starvation of the roots. We wish to emphasise that these trees would have been felled anyway during the conversion to oil palm - this project is not causing any additional deforestation.<br>The processes measured are:<br>- CO2 fluxes from soil measured with portable chambers from which a gas sample is drawn and analysed in the field with a portable instrument (CO2) <br>- Changes in tree circumference monitored with automatic dendrometer bands.<br>- Terrestrial laser scanning (T-lidar), non-destructive method to quantify the 3D structure of the forest stand.Pre-girdling data of all processes will be collected, starting at least two months before the girdling. The girdling took place in early 2016, and the monitoring continued for twelve months afterwards.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/28"><b>Tree girdling - BALI project</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC, the Ministry of Education, Youth and Sports of the Czech Republic (Grant, NE/K01627X/1, NE/G018278/1, INTER-TRANSFER LTT19018)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000-2/2 JLD.4 (3))</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=5519572">here</a></p><p><b>Files: </b>This consists of 1 file: BALI_Nottingham_Girdling_Data_2021_rev.xlsx</p><p><b>BALI_Nottingham_Girdling_Data_2021_rev.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>CO2 and H2O data</b> (described in worksheet CO2_H2O_data)</p><p>Description: Tree identity and mortality collected taken January 2016- January 2017; Soil respiration, soil temperature and January moisture measurements taken January- March 2016 in a girdled tropical forest using a LiCor 8100a </p><p>Number of fields: 12</p><p>Number of data rows: 12548</p><p>Fields: </p><ul><li><b>PlotName</b>: reference to the experiment location within the SAFE plot network (experiment took place in the 'Tower plot / SAF-05'') (Field type: location)</li><li><b>daynight</b>: defined by 6pm to 6am (Field type: categorical)</li><li><b>date</b>: date of measurement (Field type: date)</li><li><b>plot</b>: subplot' in manuscript (Field type: id)</li><li><b>Rday</b>: relative data to the start of girdling (girdling day = 0) (Field type: id)</li><li><b>CO2</b>: soil CO2 efflux (Field type: numeric)</li><li><b>H2O</b>: soil volumetric moisture (Field type: numeric)</li><li><b>T</b>: soil temperature (Field type: numeric)</li><li><b>port</b>: refers to soil collar location (we allocated chamber port to soil collar location) (Field type: id)</li><li><b>portplot</b>: soil collar location nested within plot (Field type: id)</li><li><b>time</b>: time of measurement (24h) (Field type: numeric)</li><li><b>phase</b>: measurement period (see manuscript for phase definitions) (Field type: categorical)</li></ul></li><li><p><b>Tree mortality data</b> (described in worksheet Mortality_data)</p><p>Description: Tree census of trees surroudings the points where Licor 8100a measurements were taken</p><p>Number of fields: 20</p><p>Number of data rows: 259</p><p>Fields: </p><ul><li><b>PlotName</b>: reference to the experiment location within the SAFE plot network (experiment took place in the 'Tower plot / SAF-05'') (Field type: location)</li><li><b>ForestPlotsCode</b>: reference to the experiment location within the SAFE plot network (experiment took place in the 'Tower plot / SAF-05'') (Field type: id)</li><li><b>Subplot</b>: subplots 1-12 included in the manuscript (Field type: id)</li><li><b>CensusDate</b>: date when trees were originally measured (Field type: date)</li><li><b>TagNumber</b>: tree tag identity (Field type: id)</li><li><b>Height_m</b>: tree height (Field type: numeric)</li><li><b>Comments</b>: comments about the tree (Field type: comments)</li><li><b>Family</b>: tree family (Field type: taxa)</li><li><b>Genus</b>: tree genus (Field type: taxa)</li><li><b>SpeciesName</b>: tree species (Field type: comments)</li><li><b>WoodDensity</b>: wood density (Field type: numeric)</li><li><b>CrownProjection_Area_m2_in2016</b>: Crown Projection Area in 2016 (Field type: numeric)</li><li><b>X_m</b>: coordinates (Latitude) (Field type: numeric)</li><li><b>Y_m</b>: coordinates (Longitude) (Field type: numeric)</li><li><b>GirdlingDeathDate</b>: girdling tree death date (Field type: date)</li><li><b>Biomass_kgPerStem</b>: Biomass_kgPerStem (Field type: numeric)</li><li><b>Carbon_kgCperStem</b>: Carbon_kgCperStem (Field type: numeric)</li><li><b>mortality</b>: mortality (Field type: categorical)</li><li><b>DBHgrowth_cm_year</b>: DBHgrowth_cm_year (Field type: numeric)</li><li><b>DBHAnnualGrowthRate</b>: DBHAnnualGrowthRate (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2015-08-04 to 2017-02-07</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Plantae <br> -  -  Tracheophyta <br> -  -  -  Magnoliopsida <br> -  -  -  -  Lamiales <br> -  -  -  -  -  Lamiaceae <br> -  -  -  -  -  -  <i>Callicarpa</i> <br> -  -  -  -  Rosales <br> -  -  -  -  -  Urticaceae <br> -  -  -  -  -  -  <i>Pipturus</i> <br> -  -  -  -  -  -  <i>Dendrocnide</i> <br> -  -  -  -  -  -  <i>Oreocnide</i> <br> -  -  -  -  -  Moraceae <br> -  -  -  -  -  -  <i>Ficus</i> <br> -  -  -  -  Malpighiales <br> -  -  -  -  -  Achariaceae <br> -  -  -  -  -  -  <i>Hydnocarpus</i> <br> -  -  -  -  -  Euphorbiaceae <br> -  -  -  -  -  -  <i>Macaranga</i> <br> -  -  -  -  -  -  <i>Cephalomappa</i> <br> -  -  -  -  -  -  <i>Mallotus</i> <br> -  -  -  -  -  Phyllanthaceae <br> -  -  -  -  -  -  <i>Aporosa</i> <br> -  -  -  -  -  Ixonanthaceae <br> -  -  -  -  -  -  <i>Ixonanthes</i> <br> -  -  -  -  -  Calophyllaceae <br> -  -  -  -  -  -  <i>Calophyllum</i> <br> -  -  -  -  -  Violaceae <br> -  -  -  -  -  -  <i>Rinorea</i> <br> -  -  -  -  Ericales <br> -  -  -  -  -  Pentaphylacaceae <br> -  -  -  -  -  -  <i>Adinandra</i> <br> -  -  -  -  -  Sapotaceae <br> -  -  -  -  -  -  <i>Palaquium</i> <br> -  -  -  -  -  Symplocaceae <br> -  -  -  -  -  -  <i>Symplocos</i> <br> -  -  -  -  -  Ebenaceae <br> -  -  -  -  -  -  <i>Diospyros</i> <br> -  -  -  -  Malvales <br> -  -  -  -  -  Dipterocarpaceae <br> -  -  -  -  -  -  <i>Shorea</i> <br> -  -  -  -  -  -  <i>Dipterocarpus</i> <br> -  -  -  -  -  -  <i>Dryobalanops</i> <br> -  -  -  -  -  -  <i>Parashorea</i> <br> -  -  -  -  -  Malvaceae <br> -  -  -  -  -  -  <i>Pterospermum</i> <br> -  -  -  -  -  -  <i>Scaphium</i> <br> -  -  -  -  -  -  <i>Brownlowia</i> <br> -  -  -  -  -  -  <i>Sterculia</i> <br> -  -  -  -  -  -  <i>Microcos</i> <br> -  -  -  -  -  -  <i>Neesia</i> <br> -  -  -  -  -  -  <i>Diplodiscus</i> <br> -  -  -  -  Laurales <br> -  -  -  -  -  Lauraceae <br> -  -  -  -  -  -  <i>Actinodaphne</i> <br> -  -  -  -  -  -  <i>Litsea</i> <br> -  -  -  -  -  -  <i>Eusideroxylon</i> <br> -  -  -  -  Celastrales <br> -  -  -  -  -  Celastraceae <br> -  -  -  -  -  -  <i>Lophopetalum</i> <br> -  -  -  -  Magnoliales <br> -  -  -  -  -  Myristicaceae <br> -  -  -  -  -  -  <i>Knema</i> <br> -  -  -  -  -  Annonaceae <br> -  -  -  -  -  -  <i>Goniothalamus</i> <br> -  -  -  -  -  -  <i>Polyalthia</i> <br> -  -  -  -  -  -  <i>Maasia</i> <br> -  -  -  -  Vitales <br> -  -  -  -  -  Vitaceae <br> -  -  -  -  -  -  <i>Leea</i> <br> -  -  -  -  Myrtales <br> -  -  -  -  -  Myrtaceae <br> -  -  -  -  -  -  <i>Syzygium</i> <br> -  -  -  -  -  Lythraceae <br> -  -  -  -  -  -  <i>Duabanga</i> <br> -  -  -  -  Cornales <br> -  -  -  -  -  Cornaceae <br> -  -  -  -  -  -  <i>Alangium</i> <br> -  -  -  -  Gentianales <br> -  -  -  -  -  Rubiaceae <br> -  -  -  -  -  -  <i>Neolamarckia</i> <br> -  -  -  -  -  -  <i>Neonauclea</i> <br> -  -  -  -  -  -  <i>Urophyllum</i> <br> -  -  -  -  -  -  <i>Pleiocarpidia</i> <br> -  -  -  -  Fabales <br> -  -  -  -  -  Fabaceae <br> -  -  -  -  -  -  <i>Saraca</i> <br> -  -  -  -  -  Polygalaceae <br> -  -  -  -  -  -  <i>Xanthophyllum</i> <br> -  -  -  -  Cucurbitales <br> -  -  -  -  -  Tetramelaceae <br> -  -  -  -  -  -  <i>Octomeles</i> <br> -  -  -  -  Fagales <br> -  -  -  -  -  Fagaceae <br> -  -  -  -  -  -  <i>Lithocarpus</i> <br> -  -  -  -  -  -  <i>Castanopsis</i> <br> -  -  -  -  Sapindales <br> -  -  -  -  -  Sapindaceae <br> -  -  -  -  -  -  <i>Nephelium</i> <br> -  -  -  -  -  -  <i>Dimocarpus</i> <br> -  -  -  -  -  -  <i>Pometia</i> <br> -  -  -  -  -  Meliaceae <br> -  -  -  -  -  -  <i>Dysoxylum</i> <br> -  -  -  -  -  -  <i>Aglaia</i> <br> -  -  -  -  -  Burseraceae <br> -  -  -  -  -  -  <i>Canarium</i> <br> -  -  -  -  -  Anacardiaceae <br> -  -  -  -  -  -  <i>Buchanania</i> <br></div><p></p>
Biogeographic differences in plant-soil biota relationships contribute to the invasion exotic range expansion of Verbascum thapsus
<ol> <li><span>Exotic plant species can evolve adaptations to environmental conditions in the exotic range. Furthermore, soil biota can foster exotic spread in the absence of negative soil pathogen-plant interactions or because of increased positive soil biota-plant feedbacks in the exotic range. Little is known, however, about the evolutionary dimension of plant-soil biota interactions when comparing native and introduced ranges.</span></li> <li><span>To assess the role of soil microbes for rapid evolution in plant invasion, we subjected <i>Verbascum thapsus</i>, a species native to Europe, to a reciprocal transplant experiment with soil and seed material originating from Germany (native) and New Zealand (exotic). Soil samples were treated with biocides to distinguish between effects of soil fungi and bacteria. Seedlings from each of five native and exotic populations were transplanted into soil biota communities originating from all populations and subjected to treatments of soil biota reduction: application of (i) fungicide, (ii) biocide, (iii) a combination of the two and (iv) control. </span></li> <li><span>For most of the investigated traits, native populations showed higher performance than exotic populations; there was no effect of soil biota origin. However, plants developed longer leaves and larger rosettes when treated with their respective home soil communities, indicating that native and exotic plant populations differed in their interaction with soil biota origin. The absence of fungi and bacteria resulted in a higher specific root length, suggesting that <i>V. thapsus</i> may compensate the absence of mutualistic microbes by increasing its root-soil surface contact.<b> </b></span></li> <li><span><b>Synthesis. </b>Introduced plants can evolve adaptations to soil biota in their new distribution range. This demonstrates the importance of biogeographic differences in plant-soil biota relationships and suggests that future studies addressing evolutionary divergence should account for differential effects of soil biota from the home and exotic range on native and exotic populations of successful plant invaders. </span></li> </ol>
Exposure, hazard and vulnerability all contribute to Schistosoma haematobium re-infection in northern Senegal
<p>The risk of infectious diseases, including snail-borne schistosomiasis, is determined by three interrelated components: exposure, hazard and vulnerability. For schistosomiasis, exposure occurs through behaviors involving water contact, but not without the environmental hazard of snails and parasites in the water. Socio-economic vulnerability makes it difficult to reduce exposure in the presence of hazard, because it increases reliance on hazardous activities and environments. We aimed to quantify the contributions of exposure, hazard and vulnerability to schistosome re-infection presence and intensity. We used cross-sectional parasitological data from 821 school-aged children in 13 villages along the Senegal River, survey data from 411 households where those children resided and ecological data from all 24 village water contact sites. We fit mixed-effects logistic and negative binomial regressions with indices of exposure, hazard and vulnerability as explanatory variables of <i>Schistosoma haematobium</i> infection, along with demographic control variables. Multi-model inference was used to determine the relative importance of each component of risk and model averaging was used to quantify associations between infection outcomes and indices of hazard, exposure and vulnerability. The most important component of <i>S. haematobium </i>presence was hazard (Ʃw<sub>i =</sub> 0.95), followed by vulnerability (Ʃw<sub>i</sub> = 0.91), while the most important component of <i>S. haematobium </i>intensity was exposure (Ʃw<sub>i</sub> = 1.00), followed by hazard (Ʃw<sub>i</sub> = 0.77). Hazard was positively associated with infection presence (OR = 1.49, 95% CI 1.12, 1.97), while exposure was positively associated with infection intensity. Our findings highlight how social (exposure and vulnerability) and environmental (hazard) processes act together to facilitate the acquisition and accumulation of schistosome infection from the environment across time and space. This approach can inform targeting of social and environmental interventions as complements to mass drug administration.</p>
Negative regulation of IMD contributes to disease tolerance during systemic bacterial infection in Drosophila
<p>Data and R code for <strong>Negative regulation of IMD contributes to disease tolerance during systemic bacterial infection in <em>Drosophila</em></strong></p>
Saudi scholars' contribution to the COVID-19 literature: A bibliometric study
<p>This study aims to explore Saudi researchers' academic performance on the topic of COVID-19. In addition, the paper attempts to find the contribution of Saudi Arabia in the area and how Saudi Arabia is doing compared to other Arab countries. The study results are expected to help understand the current role of Saudi Arabia in fighting COVID-19.</p>
Cardiovascular contributions and energetic costs of thermoregulation in ectothermic vertebrates
<p>Unfiltered data used in the manuscript with file descriptions. Includes R script and example datasets for reproducing figure 1, as well as animating figure 1 for the box turtle and painted turtle (body temperature - black solid line, ambient temperature - black dotted line, and heart rate - red solid line).</p>
Substantial contribution of in-situ produced bacterial sphingolipids to the sedimentary lipidome
<p><strong>Abstract:</strong> The sedimentary lipid pool comprises a myriad of components with some specific biomarkers used in paleoclimatic and geobiological reconstructions. However, a comprehensive view of the sedimentary lipidome is lacking. Here we conduct an untargeted analysis of the Black Sea sedimentary lipidome using high resolution mass spectrometry. Besides commonly reported phytoplankton-derived fossil lipids originate from oxic surface water, a diverse and abundant set of sphingolipids, accounting for ~20% of the lipidome, was discovered. These sphingolipids are produced in-situ by sedimentary anaerobic bacteria, likely in place of phospholipids due to the deficiency of phosphate in anoxic sediments. Our results suggest that while phytoplankton-derived lipids contribute 50–60% of the sedimentary lipidome, the importance of bacterial lipids, particularly in-situ produced sphingolipids, has been overlooked.</p> <p>Source data:</p> <p>Data 1. Spt_hits.and.backbone_sequences.MAFFT-L-INS-i.msa</p> <p>Data 2. Spt_hits.and.backbone_sequences.MAFFT-L-INS-i.msa.trimAl</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.