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Fig.ç21.A mblyops timorensis sp. nov., holotype, male (NSMT-Cr 21366). A, rst thoracopodal endopod (right); B, second thoracopodal endopod (le); C, seventh thoracopodal exopod (le); D, genital organ (le, lateral); E, second pleopod (right); F, fourth pleopod (right); G, uropod and telson (dorsal); H, uropodal endopod (right, ventral); I, telson (dorsal). in The Genus Amblyops (Crustacea: Mysida: Mysidae: Erythropinae) from East Asia and Australia, with Descriptions of Ten New Species
Fig.ç21.A mblyops timorensis sp. nov., holotype, male (NSMT-Cr 21366). A, rst thoracopodal endopod (right); B, second thoracopodal endopod (le); C, seventh thoracopodal exopod (le); D, genital organ (le, lateral); E, second pleopod (right); F, fourth pleopod (right); G, uropod and telson (dorsal); H, uropodal endopod (right, ventral); I, telson (dorsal).
Data for: Speciation in kleptoparasites of oak gall wasps often correlates with shifts into new tree habitats, tree organs, or gall morphospace
<p><span>Host shifts to new plants can drive speciation for plant-feeding insects, but how commonly do host shifts also drive diversification for the parasites of those same insects? Oak gall wasps induce galls on oak trees, and shifts to novel tree hosts and new tree organs have been implicated as drivers of oak gall wasp speciation. Gall wasps are themselves attacked by many insect parasites, which must find their hosts on the correct tree species and organ, but which also must navigate the morphologically variable galls with which they interact. Thus, we ask whether host shifts to new trees, organs, or gall morphologies correlate with gall parasite diversification. We delimit species and infer phylogenies for two genera of gall kleptoparasites, <em>Synergus</em> and <em>Ceroptres</em>, reared from a variety of North American oak galls. We find that most species were reared from galls induced by just one gall wasp species, and no parasite species was reared from galls of more than four species. Most kleptoparasite divergence events correlate with shifts to non-ancestral galls. These shifts often involved changes in tree habitat, gall location, and gall morphology. Host shifts are thus implicated in driving diversification for both oak gall wasps and their kleptoparasitic associates.</span></p>
Computationally directed manipulation of cross-linked covalent organic frameworks for membrane applications - PCCP
<p>The dataset uploaded herein is associated with the paper published under the title "<i>Computationally directed manipulation of cross-linked covalent organic frameworks for membrane applications</i>" with the Royal Society of Chemistry - Physical Chemistry Chemical Physics Journal. This dataset includes the .vasp files for all modeled structures, an example dftb_in.hsd file, which is the instructional file for geometry optimization with DFTB+, an Excel spreadsheet with atom number densities and total energy values for all modeled geometries, and, finally, a Python script that was used to calculate the Enthalpy of Formation and Cohesive Energies for all structures. This data has been made available to the scientific community in the interest of open-source and accessible data. The authors request that you please cite the associated paper and Zenodo dataset if used.</p><p><strong>Abstract</strong></p><p>Two-dimensional covalent organic frameworks (2D-COFs) exhibit characteristics ideal for membrane applications, such as high stability, tunability and porosity along with well-ordered nanopores. However, one of the many challenges with fabricating these materials into membranes is that membrane wetting can result in layer swelling. This allows molecules that would be excluded based on pore size to flow around the layers of the COF, resulting reduced separation. Cross-linking between these layers inhibits swelling to improve the selectivity of these membranes. In this work, computational models were generated for a quinoxaline-based COF cross-linked with oxalyl chloride (OC) and hexafluoroglutaryl chloride (HFG). Enthalpy of formation and cohesive energy calculations from these models show that formation of these COFs is thermodynamically favorable and the resulting materials are stable. The cross-linked COF with HFG was synthesized and characterized with Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), thermogravimetric analysis with differential scanning calorimetry (TGA-DSC), and water contact angles. Additionally, these frameworks were fabricated into membranes for permeance testing. The experimental data supports the presence of cross-linking and demonstrates that varying the amount of HFG used in the reaction does not change the amount of cross-linking present. Computational models indicate that the effect of varying cross-linking concentration on the framework stability is negligible and less cross-linking still results in stable materials. This work sheds light on the nature of the cross-linking in these 2D-COFs and their application in membrane separations.</p>
Figs. 65–70. Oonops pulcher Templeton, female. 65. Tarsal organ from palp, dorsal view. 66. Epigastric region, ventral view. 67. Spinnerets, posterior view. 68. Same, anterior lateral spinneret. 69. Same, posterior median spinnerets. 70 in The Goblin Spider Genus Heteroonops (Araneae, Oonopidae), With Notes on Oonops
Figs. 65–70. Oonops pulcher Templeton, female. 65. Tarsal organ from palp, dorsal view. 66. Epigastric region, ventral view. 67. Spinnerets, posterior view. 68. Same, anterior lateral spinneret. 69. Same, posterior median spinnerets. 70. Same, posterior lateral spinneret.
Figs. 25–32. Oonops pulcher Templeton, male. 25. Tarsal organ from leg III, dorsal view. 26. Same, leg IV. 27. Spinnerets, posterior view. 28. Same, anterior lateral spinneret. 29. Same, posterior median spinneret. 30. Same, posterior lateral spinneret. 31. Palp, prolateral view. 32 in The Goblin Spider Genus Heteroonops (Araneae, Oonopidae), With Notes on Oonops
Figs. 25–32. Oonops pulcher Templeton, male. 25. Tarsal organ from leg III, dorsal view. 26. Same, leg IV. 27. Spinnerets, posterior view. 28. Same, anterior lateral spinneret. 29. Same, posterior median spinneret. 30. Same, posterior lateral spinneret. 31. Palp, prolateral view. 32. Same, retrolateral view.
Arctic Rivers Dissolved Organic Carbon River Export Analysis
<p>This repository has data for the estimation of dissolved organic carbon and colored dissolved organic carbon in the 6 Great Arctic Rivers. The data has been derived from the arcticgreatrivers.org repository for use in the USGS LOADEST model https://water.usgs.gov/software/loadest/ to predict river mass load as a function of measured discharge. The *_discharge.dat files contain the river discharge data from arcticgreatrivers.org and each *.tar directory with the river's name contain the output file from the LOADEST model with 100 model runs each for each parameter defined below. The netcdf file ArcticRivers_CarbonTrends.nc contains all of the LOADEST model prediction ensembles and mean/total seasonal values used in the trend analysis.</p> <p>DOC=Dissolved organic carbon (mg/L)</p> <p>CDOC=Colored dissolved organic carbon (mg/L)</p> <p>S1=CDOM absorption spectral slope between 275-295 nm (1/nm)</p> <p>S2=CDOM absorption spectral slope between 350-400 nm (1/nm)</p> <p>a300 = CDOM absorption at 300 nm (1/m)</p> <p>There is also a file River_CDOM_PUB.mat that is a MATLAB data structure with the data used to construct the LOADEST model input files.</p> <p>Dr. J. Blake Clark should be contacted at bclark@umbc.edu with any specific questions.</p>
Organ-on-a-Chip (OOC) Image Dataset
<p><strong>Overview: </strong>This dataset contains 3000+ images generated from OOC (organ-on-a-chip) setup with different cell types. The images were generated by an automated brightfield microscopy setup; for each image, such parameters as cell type, time after seeding, and class label ('good' or 'bad' sample quality as assessed by a biology expert) are provided. Furthermore, for some images, seeding density and flow rate are given as well. The dataset can be used for training machine learning classifiers for the automated analysis of the data generated with OOC setup, allowing to create more reliable tissue models and automate decision making processes for growing OOC.</p><p>The dataset comprises images of OOC samples from the following cell lines:</p><ul><li>A549 (human lung adenocarcinoma alveolar basal epithelial cells, CCL-185, ATTC)</li><li>Caco-2 (colorectal adenocarcinoma epithelial cells, HTB-37, ATCC)</li><li>HPMEC (human pulmonary microvascular endothelial cells; 3000, ScienCell)</li><li>HUVEC (human umbilical vein endothelial cells, CRL-1730, ATCC)</li><li>NHBE (normal human bronchial epithelial cells, CC-2541, Lonza)</li><li>HSAEC (human small airway epithelial cells, PCS-301-010, ATCC)</li></ul><p><strong>Structure of the dataset:</strong> The dataset is split into three main folders that correspond to the data split for training machine learning models, i.e., 'train', 'val', and 'test'. The train/val/test split is done proportionally with respect to the class labels, cell lines, and time after seeding (see below), yet the data can be split or merged in other ways to suit the needs of prospective users of the dataset. Within each of the main folders, there are a 'bad' and a 'good' folder with the images corresponding to the respective class labels (see 'Overview' above). The images in 'bad' / 'ģood' folders are further subdivided into folders corresponding to respective cell lines, which are in their turn subdivided into folders corresponding to the different times after seeding. Therefore, it is easy to find images of interest, e.g., '4+ days' 'good' images of the cell line A549 from the 'train' dataset. Further information about the images is available in the file 'OOC_datasheet.xlsx'. </p><p><strong>Acknowledgement:</strong> The work presented in this paper was supported by the project 'AI-improved organ on chip cultivation for personalised medicine (AimOOC)' (contract with Central Finance and Contracting Agency of Republic of Latvia no. 1.1.1.1/21/A/079; the project is co-financed by REACT-EU funding for mitigating the consequences of the pandemic crisis).</p>
Quantification of soil organic carbon: the challenge of biochar-induced spatial heterogeneity
<p>R-script and output from model on spatially discrete biochar application and its influence on representative SOC sampling. An additional document to explain the data curation is also available ("Comment on Data curation").</p><p> </p>
Fig. 11 in Viuria Grishin, 2019 (Lepidoptera: Hesperiidae): taxonomy, description of two new species, and remarks on the morphology of secondary sexual organs of males
Fig. 11. Male genitalia of Viuria acadia sp. nov., paratype (DZ 47.183). A–D. Uncus and tegumen. A. Dorsal view. B. Ventral view. C. Right lateral view. D. Left lateral view. E. Valva, left lateral inner view. F. Valva, right lateral inner view. Scale bar: 500 µm.
Fig. 10 in Viuria Grishin, 2019 (Lepidoptera: Hesperiidae): taxonomy, description of two new species, and remarks on the morphology of secondary sexual organs of males
Fig. 10. Male genitalia of Viuria acadia sp. nov., paratype (DZ 47.183). A. Left lateral view. B. Right lateral view. C. Dorsal view. D. Ventral view. E. Aedeagus, dorsal view. F. Aedeagus, lateral view. Scale bar: 500 µm.
Fig. 9 in Viuria Grishin, 2019 (Lepidoptera: Hesperiidae): taxonomy, description of two new species, and remarks on the morphology of secondary sexual organs of males
Fig. 9. Male genitalia of Viuria innana sp. nov., paratype (DZ 47.172). A–D. Uncus and tegumen. A. Dorsal view. B. Ventral view. C. Left lateral view. E. Valva, left lateral inner view. F. Valva, right lateral inner view. Scale bar: 500 µm.
Fig. 8 in Viuria Grishin, 2019 (Lepidoptera: Hesperiidae): taxonomy, description of two new species, and remarks on the morphology of secondary sexual organs of males
Fig. 8. Male genitalia of Viuria innana sp. nov., paratype (DZ 47.172) A. Left lateral view. B. Right lateral view. C. Dorsal view. D. Ventral view. E. Aedeagus, dorsal view. E. Aedeagus, lateral view. Scale bar: 500 µm.
Fig. 7 in Viuria Grishin, 2019 (Lepidoptera: Hesperiidae): taxonomy, description of two new species, and remarks on the morphology of secondary sexual organs of males
Fig. 7. Male genitalia of Viuria lista (Evans, 1953) (OM 4.507). A–D. Uncus and tegumen. A. Dorsal view. B. Ventral view. C. Left lateral view. D. Right lateral view. E. Valva, left lateral inner view. F. Valva, right lateral inner view. Scale bar: 500 µm.
Fig. 13 in Viuria Grishin, 2019 (Lepidoptera: Hesperiidae): taxonomy, description of two new species, and remarks on the morphology of secondary sexual organs of males
Fig. 13. Patches of modified scales in DHW of species of Viuria Grishin, 2019. A–D. Viuria lista (Evans, 1953). E–F. Viuria licisca (Plötz, 1882). A. Overall view. B. Detail of the modified greyish scales in the swollen vein Sc+R1. C. Details of the creamy scales near the base of the swollen veins Rs and M1. D–F. Same sequence with diaphanized wings.
Fig. 5 in Viuria Grishin, 2019 (Lepidoptera: Hesperiidae): taxonomy, description of two new species, and remarks on the morphology of secondary sexual organs of males
Fig. 5. Male genitalia of Viuria licisca (Plötz, 1882) (OM 43.281). A–D. Uncus and tegumen. A. Dorsal view. B. Ventral view. C. Left lateral view. D. Right lateral view. E. Valva, left lateral inner view. F. Valva, right lateral inner view. Scale bar: 500 µm.
Fig. 4 in Viuria Grishin, 2019 (Lepidoptera: Hesperiidae): taxonomy, description of two new species, and remarks on the morphology of secondary sexual organs of males
Fig. 4. Male genitalia of Viuria licisca (Plötz, 1882) (OM 43.776). A. Left lateral view. B. Right lateral view. C. Dorsal view. D. Ventral view. E. Aedeagus, lateral view. F. Aedaegus, dorsal view. Scale bar: 500 µm.
Fig. 2 in Viuria Grishin, 2019 (Lepidoptera: Hesperiidae): taxonomy, description of two new species, and remarks on the morphology of secondary sexual organs of males
Fig. 2. Male genitalia of Viuria herophile (Harward, 1914) (DZ 9.681). A. Left lateral view. B. Right lateral view. C. Dorsal view. D. Ventral view. E. Aedeagus, dorsal view. F. Aedeagus lateral view. Scale bar: 500 µm.
Fig. 3 in Viuria Grishin, 2019 (Lepidoptera: Hesperiidae): taxonomy, description of two new species, and remarks on the morphology of secondary sexual organs of males
Fig. 3. Male genitalia of Viuria herophile (Harward, 1914) (DZ 9.677). A–D. Uncus and tegumen. A. Dorsal view. B. Ventral view. C. Left lateral view. D. Right lateral view. E. Valva, left lateral inner view. F. Valva, right lateral inner view. Scale bar: 500 µm.
Fig. 1 in Viuria Grishin, 2019 (Lepidoptera: Hesperiidae): taxonomy, description of two new species, and remarks on the morphology of secondary sexual organs of males
Fig. 1. Species of Viuria Grishin, 2019, dorsal and ventral views. A–D. V. herophile (Harward, 1914). A–B. ♂, Peru, Madre de Dios, Parque Manu, Pakitza (DZ 52.466). C–D. ♀, Brazil, Acre, 50 km NO of Bujari (DZ 47.221). E–H. V. licisca (Plötz, 1882). E–F. ♀, Costa Rica, San José, Ciudad Colón (OM 26.945). G–H. ♂, Mexico, Oaxaca, Candelaria Loxicha (DZ 47.162). I–L. V. lista (Evans, 1953). I–J. ♀, Brazil, Rondônia, 58 km W of Ariquemes (OM 14.507). K–L. ♀, Brazil, Rondônia, 58 km W of Ariquemes, (OM 14.504). M–P. V. inanna sp. nov. M–N. ♂, holotype (DZ 52.489), Brazil, Paraná, Fênix. O–P. ♀ (DZ 52.492), Brazil, Paraná, Fênix. Q–T. V. acadia sp. nov. Q–R. ♂, holotype (DZ 52.520), Brazil, Mato Grosso, 31 km NO of Barra do Bugres. S–T. ♀ (DZ 52.517), Brazil, Mato Grosso, 31–35 km NO of Barra do Bugres.
Organic electrochemical transistor aptasensor for interleukin-6 detection
<p>Explanation of included data for following publication:</p><p>-------------------------------------------------------------------------------------</p><p><strong>Organic electrochemical transistor aptasensor for interleukin-6 detection</strong></p><p>Chiara Diacci (1,2), Bernhard Burtscher (1), Marcello Berto (2), Tero Petri Ruoko (1), Samuel Lienemann (1), Pierpaolo Greco (3,4), Magnus Berggren (1), Marco Borsari (5), Daniel T. Simon (1), Carlo A. Bortolotti (2), Fabio Biscarini (2,4)</p><p> </p><p>1) Laboratory of Organic Electronics, Department of Science and Technology, ITN, Linköping University, 601 74, Norrköping, Sweden.</p><p>2) Dipartimento di Scienze della Vita, Università di Modena e Reggio Emilia, via Campi 103, 41125 Modena, Italy.</p><p>3) Department of Neuroscience and Rehabilitation, Università di Ferrara, Via Borsari 46, 44121 Ferrara, Italy.</p><p>4) Center for Translational Neurophysiology of Speech and Communication, Istituto Italiano di Tecnologia, via Fossato di Mortara 17-193, 44100 Ferrara, Italy.</p><p>5) Dipartimento di Scienze Chimiche e Geologiche, Università di Modena e Reggio Emilia, via Campi 103, 41125 Modena, Italy.</p><p>-------------------------------------------------------------------------------------</p><p><strong>Manuscript</strong></p><p> </p><p>Figure 2:</p><ul><li>SEM pictures</li></ul><p> </p><p>Figure 3:</p><ul><li>CV for aptamer deposition (Time, Voltage, Current) in 1µM aptamer solution from 0V to -1V, 50mV/s for 30 cycles</li><li>Impedance data for AuNP/PEDOT:PSS electrode (before aptamer deposition) and after aptamer deposition (Aptamer/AuNP/PEDOT:PSS); (Freq/Hz, Re(Z)/Ohm, -Im(Z)/Ohm, |Z|/Ohm, Phase/deg) in ferricyanide 5mM</li></ul><p> </p><p>Figure 4: Fluorescent images (LSM, as czi format) for</p><ul><li>4a: AuNP/PEDOT:PSS (no Cy3-tag, control)</li><li>4b: Cy3-aptamer/AuNP/PEDOT:PSS (sample)</li><li>4c: Cy3-aptamer/PEDOT:PSS (no AuNP, control)</li></ul><p> </p><p>Figure 5: Data sets of transfer curves for multiple OECTs for various concentrations of IL6/TNF</p><ul><li>One sheet refers to one OECT sample with either IL6 or TNF</li><li>The first coloumn indicates the gate voltage and the others the drain current with various concentration except the second column (Tween) with is without any analyte</li><li>VDS = -0.3V</li><li>VGS swept from 0 to 0.6V</li></ul><p> </p><p> </p><p> </p><p>Supporting Information:</p><p> </p><p>Figure S1: AuNP/PEDOT:PSS electrical characteristics</p><ul><li>S1a: CV in 5mM ferricyanide from -0.5V to 0.5V at 50mV/s</li><li>S1b: Transconductance for bare Au, PEDOT:PSS in PBS 10mM</li><li>S1c: Transfer curves in PBS 10mM</li></ul><p> </p><p>Figure S2: FTIR-ATR measurements</p><ul><li>Raw exported files from the FTIR (Au+PEDOT, Au+DNA, Au+PEDOT+NP, Au+PEDOT+NP+DNA) as txt file.</li><li>For Au+DNA the normalized with the maximum and for the others the 1010cm-1 peak used for normalization. This is shown in the ATR.xlsx file.</li></ul><p> </p><p>Figure S3: IL6 detection on AuNP/PEDOT:PSS gate</p><ul><li>Transfer Charasteristics of the AuNP/PEDOT:PSS electrode with increasing IL6 concentration with VDS=-0.3V and VGS from 0V to 0.6V</li></ul><p> </p><p>Figure S4: Transconductance of OECT based aptasensor</p><ul><li>Datasets used for S4 are identical with the ones from Figure 5</li></ul><p> </p><p>Figure S5: Impedance of functionalized device with IL6</p><ul><li>Each sheet is for a IL6 concentration. The normalized OECT concentration is the same as in Figure 5.</li></ul>
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.