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Fig. 8 in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Fig. 8. Tapinesthis inermis, SEM views of ♀ genitalia (PBI 33267). A. Overview of genital area, dorsal view. B. Same, close up. C. Same, view slightly anterior. D. Anterior receptaculum (ARe), close up, arrow indicates gland ducts (GD). E. Same, anterior view. F. Same, detail of gland ducts (arrows). G. Lateral protrusion (Pr), detail, dorsal view. – ARe: Anterior Receptaculum; AS: Anterior Sclerite; FA: Flattened Apodeme; GD: Gland Duct; Pr: lateral Protrusion; PRe: Posterior Receptaculum; T: transverse sclerite.
Fig. 6. Tapinesthis inermis, SEM views. A in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Fig. 6. Tapinesthis inermis, SEM views. A. Ƌ (PBI 08123), carapace, antero-lateral view. B. Same, close up on the distribution of the smooth platelets. C. Ƌ (PBI 33564), sternum, ventral view. D. Same, mouthparts. E. Same, close up of endite setae. F. Same, cheliceral fangs. G. Ƌ (PBI 08123), endites, anterior view with detail of serrula. H. Ƌ (PBI 33564), genital region, ventral view.
Fig. 5 in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Fig. 5. Tapinesthis inermis, genitalia of ♀ (PBI 33267) A. Genital area, ventral view. B. Same, after digestion, ventral view. C. Same, dorsal view. D. Same, anterior view. – ARe: Anterior Receptaculum; AS: Anterior Sclerite; FA: Flattened Apodeme; Pr: lateral Protrusion; PRe: Posterior Receptaculum.
Fig. 3 in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Fig. 3. Tapinesthis inermis, Ƌ from the Czech Republic (PBI 32902). A. Habitus, dorsal view. B. Same, lateral view. C. Same, lateral view. D. Prosoma, ventral view.
Figs. 10 in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Figs. 10. Tapinesthis inermis, SEM views of tarsal claws of Ƌ (PBI 33564). A. Leg I, anterior view. B. Same, lateral view. C. Leg II, lateral view. D. Same, anterior view. E. Leg III, lateral view. F. Leg IV, lateral view.
Fig. 4. Tapinesthis inermis. – A – C in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Fig. 4. Tapinesthis inermis. – A – C. Ƌ (PBI 33564), palp. A. Retrolateral view. B. Same, frontal view. C. Same, prolateral view. – D. Ƌ (PBI 09021), palp, prolateral view, transmitted light. – E. Ƌ palp, lateral view after Saaristo & Marusik, 2009 (modiFed). – D: duct; Te: tendon; Tw: thin-walled duct; V: vesicle.
Fig. 12 in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Fig. 12. Tapinesthis inermis Ƌ (PBI 33564), SEM views. A. Cymbium, frontal view. B. Same. C. Metatarsus II, dorsal view, black arrow showing pore and white arrow indicating eye-shaped structure. D. Metatarsus IV, black arrow as in C. E. Trichobothrium, dorsal view of bothrium on tibia IV. F. Dorsal view of bothrium on metatarsus.
Fig. 2. Tapinesthis inermis. A in Redescription of Tapinesthis inermis (Araneae, Oonopidae), with detailed information on its ultrastructure
Fig. 2. Tapinesthis inermis. A. Ƌ (PBI 33564), habitus, dorsal view. B. Same, carapace. C. Same, sternum, ventral view. D. ♀ (PBI 33267), habitus, dorsal view. E. Ƌ (PBI 33564), habitus, lateral view. F. Same, ♀ (PBI 33267). G. ♀ (PBI 08123), carapace, anterior view. Scale bars = 0.5 mm.
Supplement_Stochastic_Reconstruction_and_Interpolation_of_Precipitation_Fields_Using_Combinded_Information_CML_and_RG
<p>This file includes the synthetic dataset used in the study "Stochastic Reconstruction and Interpolation of Precipitation Fields Using Combined Information of Commercial Microwave Links and Rain Gauges", including the full synthetic reference fields (VR_precip_h_30052013_02062013.nc) as well as the used synthetic rain gauge (precip_VRObs_dry+wet_30052013_02062013_Pgr1mm.csv) and commercial microwave links (VROBS_MWL_selection_Pgr1mm.csv) input data.</p> <p>The simulated ensembles of possible realizations of the precipitation fields are stored for the synthetic (simulation_VR) and real (simulation_realworld) world dataset. A corresponding description of the grid can be found in gridinfo.txt.</p>
Dataset for a physics informed deep learning method with adaptively weighted loss for modeling soil water flows
<p>The data for the 11 scenarios generated by Hydrus-1D is located in data.zip</p> <p>The code for the physics-informed neural networks with adaptively weighted loss used to simulate water flow in loam soils is located at PINN_adaptively_weighted_loss_loam.zip</p>
The dataset archives of SIDERITE (siderophore information database)
<p>The update of <a href="https://siderite.bdainformatics.org/">SIDERITE</a>.</p> <p>It covers all 918 known siderophores from 1635 records as of Jan 2025.</p> <p dir="auto"><strong>SIDERITE_records_20250131.xlsx</strong><br>contains 1635 siderophore records</p> <p dir="auto"><strong>SIDERITE_unique_structures_20250131.xlsx</strong><br>contains 918 unique siderophore structures</p> <p>The same siderophore structures can be found in different species, leading to multiple records. Therefore, the number of siderophore records can exceed the number of unique siderophore structures.</p> <p><strong>Non-natural_siderophore_records_20250131.xlsx</strong><br>As SIDERITE is a natural product database, it doesn’t include artificial siderophores, those synthesized by supplementing precursors, or variants produced by gene knock-out strains, though we record their names and references as supplements.</p> <p>If you find SIDERITE useful, please cite:</p> <p>Ruolin He, Shaohua Gu, Jiazheng Xu, et al. 2024. “ SIDERITE: Unveiling Hidden Siderophore Diversity in the Chemical Space Through Digital Exploration.” iMeta e192. <a href="https://doi.org/10.1002/imt2.192">https://doi.org/10.1002/imt2.192</a></p> <div> </div> <div><span><span><span>Ruolin</span> <span>He</span></span>, <span><span>Jiazheng</span> <span>Xu</span></span>, <span><span>Jiqi</span> <span>Shao</span></span>, et al.<span><span> 2025. " Tracing Siderophore Precursors to Primary Metabolism for Ecological Applications. " </span></span></span><span>bioRxiv.</span><span> <a href="https://doi.org/10.1101/2025.04.30.651379">https://doi.org/10.1101/2025.04.30.651379</a></span></div>
Data and code for the manuscript "Internal vs Forced Variability Metrics for General Circulation Models Using Information Theory"
<p>Data and code for the manuscript "Internal vs Forced Variability Metrics for General Circulation Models Using Information Theory" published in the Journal of Geophysical Research Oceans. <br>URL of the manuscript: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JC020101<br>DOI of the manuscript: https://doi.org/10.1029/2023JC020101</p>
Supplementary Information for the manuscript: "Gene expression evolution is predicted by stronger selection at more pleiotropic genes"
<p>This repository contains the supplementary information for the manuscript "<em>Gene expression evolution is predicted by stronger selection at more pleiotropic genes</em>" (<a href="https://doi.org/10.1101/2024.07.22.604294">https://doi.org/10.1101/2024.07.22.604294</a>).</p> <p>The supplementary data "data.tar.gz" is related to the Github repository <a href="https://github.com/charlesrocabert/Koch-et-al-Gene-expression-evolution-is-predictable-and-driven-by-indirect-selection-pressures">https://github.com/charlesrocabert/Koch-et-al-Predictability-of-Gene-Expression</a>.</p> <h2>Content of the repository</h2> <ul> <li><strong>Script S1. </strong>BSFG estimates (50.9 MB).</li> <li><strong>Script S2.</strong> WGCNA analysis and results G1 (99.6 MB).</li> <li><strong>Data S1.</strong> Datasets resulting from the global genomics analysis of the output of the transcriptomics pipeline (5.9 MB).</li> <li><strong>Data S2.</strong> VCF file containing the 566,296 quality-checked SNPs (1.1 GB).</li> <li><strong>Data S3.</strong> VCF file containing the 358,142 SNPs with a call rate higher or equal to 50% (964.3 MB).</li> <li><strong>Data S4.</strong> VCF file containing the imputed genotypes (964.3 MB).</li> <li><strong>Data S5.</strong> Results of the imputation tests (479.5 kB).</li> <li><strong>Data S6.</strong> VCF file containing imputed genotypes where SNPs with a minor allele frequency lower than 0.05 have been filtered out (MAF $\leq$ 0.05) (176.0 MB).</li> <li><strong>Data S7.</strong> List of the 1,273 significant eQTL associations (for gene expression levels or relative fitness as phenotypes) (61.2 kB).</li> <li><strong>Data S8.</strong> List of allele frequency changes (AFCs) for every markers in HD environment, for lines L1, L2, L3, L5, L6, Mx1 and Mx2 (32.4 MB).</li> <li><strong>Data S9.</strong> List of all SNPs indicating if their AFC is significantly higher in each line and their degree of parallelism (2.4 MB).</li> <li><strong>Data S10.</strong> Excel file containing the results of the gene functional enrichment analysis of the hub and eQTL carrier genes (33.7 KB).</li> <li><strong>data.tar.gz.</strong> Dataset mandatory to re-run the genomics analysis (see <a href="https://github.com/charlesrocabert/Koch-et-al-Gene-expression-evolution-is-predictable-and-driven-by-indirect-selection-pressures">https://github.com/charlesrocabert/Koch-et-al-Predictability-of-Gene-Expression</a>) (7.8 GB).</li> </ul> <p> </p>
Hydraulic Tomography Estimates Improved by Zonal Information from the Clustering of Geophysical Survey Data
<p>The spreadsheet contains the information of hydraulic conductivity fields, drawdown calibration and validation data sets, and tracer data sets used to accomplish the research article with the same title.</p>
Online Companion: A Preference Informed Energy Sharing Framework for a Centralized Energy Community
<p>This manuscript serves as an electronic companion to [1]. We present the complementary input data, including prosumers target demand, PV power generation, and electricity market prices.</p><p> </p><p> </p>
Challenges in providing multilingual information access
Open the record for dataset details and reuse information.
Training information and results of KarmaLoop
Open the record for dataset details and reuse information.
A systems analysis of sustainability impacts of agricultural policies in India - Supporting Information
<p>This Supporting Information provides additional details about the HTE framework applied to study sustainability challenges and interventions in the rice-wheat cropping system of Punjab, India. It includes: </p><p>Supp. Info Word document with: </p><ul><li>Text S1-S2 (Table S1-S3) on detailed quantitative model set-up and model validation and sensitivity analysis results</li><li>Text S3-S4 (Table S4-S5) on methods used to evaluate the impacts of interventions on interactions, specifying direct (structural) and indirect (quantitative) changes as well as sustainability benefits using the inclusive wealth approach.</li><li>Text S5 on expert interviews conducted to inform choice of policy options analyzed in this work </li></ul><p> </p><p>Supp. Info Excel spreadsheet Data Set S1 with the following tables:</p><p>Data Table SD1: List of system components and their attributes</p><p>Data Table SD2: Detailed interaction matrix between system components</p><p>Data Table SD3: Attributes of crops and residues: crop production, protein content and residue generation</p><p>Data Table SD4: Attributes of crops: use of agricultural inputs for crop production </p><p>Data Table SD5: Attributes of technical components: Emission factors and GWP</p><p>Data Table SD6: Values of system components' attributes at t=1 (year=2019)</p><p>Data Tables SD7-SD14: Detailed quantitative impacts of interventions on sustainability metrics</p>
Supplementary Information: Video data files: Gray et al. Caught on camera: Ocelot, Leopardus pardalis (Mammalia: Felidae) predation on foam nests of Savage's thin-toed frog, Leptodactylus savagei (Amphibia: Leptodactylidae)
<p>Supplementary Information: Original camera trap footage for Gray, Ibáñez, Barrios & Potvin: Caught on camera: Ocelot, <i>Leopardus pardalis </i>(Mammalia: Felidae) predation on foam nests of Savage's thin-toed frog, <i>Leptodactylus savagei</i> (Amphibia: Leptodactylidae). </p><p> </p>
Is epistemological purism possible in Library and Information Science research?
<p><strong>Is epistemological purism possible in Library and Information Science research?</strong></p><p>Several scholars in Knowledge Organization (KO) and in Library and Information Science (LIS) have expressed concern that the field seems driven primarily by practical applications and professional practices. Others have stressed the need for research in the field to be more theoretically motivated, lest it continue to be perceived not as a scientific field but simply as a set of techniques used to solve problems. Hence, there have been calls for scholars and practitioners to make explicit the epistemological assumptions underlying methods used to design information services and tools for their users as this can help identify inherent biases. </p><p>In answer to these calls, we examined the epistemological assumptions underlying methods deployed in some fields of KO and LIS – namely information retrieval and topic mapping. A careful examination of the underlying assumptions to these methods showed that they exhibited epistemological hybridity, in this case, a combination of empiricism and positivism with a dash of pragmatism and hermeneutics at the end. </p><p>We therefore hypothesize that it may not be a straightforward task for a researcher or an information professional to claim that a method borrows solely from positivism, rationalism, empiricism or social-constructivism or historicism or hermeneutics, simply because there is a gap between epistemological theories and methods linked to them. Also, a given piece of work may deploy methods linked to different epistemological theories.</p><p>Although our examples are taken from LIS and KO fields, we suspect that our observations may be generalized to other scientific disciplines and fields of inquiry.</p>
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.