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FIGURE 12. Trophoniphila bradyi M in Enigmas from the past: M'Intosh's (1885) annelidicolous copepods from the voyage of H. M. S. Challenger
FIGURE 12. Trophoniphila bradyi M'Intosh, 1885 (♀). A, ectosoma and left egg-sac; B, ectosoma, posterior (c.p., copulatory pore; e.s., egg sac string; g.a., genital aperture; g.p., genital papillae; s.r., seminal receptacle; P6, genital operculum derived from sixth leg); C, same, ventral (c.d., copulatory duct; c.p., copulatory pore; s.r., seminal receptacle; P6, genital operculum derived from sixth leg); D, same, lateral (arrow indicating connection with endosoma); E, same, anterior, showing connection with endosome (arrowed stippled area) [inset showing detail of integumental pore].
FIGURE 6. Leaniricola rotundata M in Enigmas from the past: M'Intosh's (1885) annelidicolous copepods from the voyage of H. M. S. Challenger
FIGURE 6. Leaniricola rotundata M'Intosh, 1885 (♀). A, leg 1, anterior; B, leg 2, anterior; C, leg 3, anterior; D, leg 4.
FIGURE 5. Leaniricola rotundata M in Enigmas from the past: M'Intosh's (1885) annelidicolous copepods from the voyage of H. M. S. Challenger
FIGURE 5. Leaniricola rotundata M'Intosh, 1885 (♀). A, antennule; B, antenna; C, maxilla; D, maxilliped. Roman numerals in A referring to ancestral antennulary segments (Huys & Boxshall 1991).
FIGURE 3. Leaniricola rotundata M in Enigmas from the past: M'Intosh's (1885) annelidicolous copepods from the voyage of H. M. S. Challenger
FIGURE 3. Leaniricola rotundata M'Intosh, 1885 (♀). A, habitus, lateral; B, urosome, dorsal; C, urosome, lateral.
FIGURE 1 in Enigmas from the past: M'Intosh's (1885) annelidicolous copepods from the voyage of H. M. S. Challenger
FIGURE 1. Parasitic copepods described by M'Intosh (1885). A, Leaniricola rotundata; B, Praxillinicola kroyeri; C, Oestrella levinseni; D, Trophoniphila bradyi.
FIGURE 2. Leaniricola rotundata M in Enigmas from the past: M'Intosh's (1885) annelidicolous copepods from the voyage of H. M. S. Challenger
FIGURE 2. Leaniricola rotundata M'Intosh, 1885 (♀). A, habitus, dorsal (left egg-sac removed); B, cephalothoracic region, ventral (distal portion of labrum and mandibular gnathobases omitted).
FIGURE 8. Praxillinicola kroyeri M in Enigmas from the past: M'Intosh's (1885) annelidicolous copepods from the voyage of H. M. S. Challenger
FIGURE 8. Praxillinicola kroyeri M'Intosh, 1885 (♀). A, right antennule (A1) and antenna (A2, in reflexed condition); B, same on left side (antennulary claw broken); C, right antennule; D, antennary endopod (right side); E, oral area, showing rostral projection (r.p.), labral retractor muscles (r.m.; only one of three pairs drawn), labral hooks (l.h.) and sensilla (s.); F, maxilliped, anterior; G, maxillipedal endopod, posterior; H, anal somite, showing right genital aperture (g.a.) and anal slit (a.s.), dorsal.
FIGURE 7. Praxillinicola kroyeri M in Enigmas from the past: M'Intosh's (1885) annelidicolous copepods from the voyage of H. M. S. Challenger
FIGURE 7. Praxillinicola kroyeri M'Intosh, 1885 (♀). A, habitus, dorsal; B, same, ventral, showing position of rostral projection (r.p.), socket containing antennule and antenna (A1 + A2), labrum (Lb.) and maxilliped (Mxp); C, median copulatory pores (c.p.) with remnant of spermatophore neck (r.s.) indicated, ventral.
FIGURE 4. Leaniricola rotundata M in Enigmas from the past: M'Intosh's (1885) annelidicolous copepods from the voyage of H. M. S. Challenger
FIGURE 4. Leaniricola rotundata M'Intosh, 1885 (♀). A, Oral area showing labrum (Lb.), labium (Lm.), mandible (G.: gnathobase) and maxillule (Mx1) [left gnathobase omitted], posterior view; B, same, left lateral view (A2: coxobasis of antenna); C, right caudal ramus, lateral; D, distal portion of left caudal ramus, dorsal.
FIGURE 2 in When starvation challenges the tradition of niche conservatism: On a new species of the saxicolous genus Phymaturus from Patagonia Argentina with pseudoarboreal foraging behaviour (Iguania, Liolaemidae)
FIGURE 2. (a) Adult female paratype of Phymaturus agilis in life from Terra Typica, Río Negro, ventral view. Photo: F.O. Tappari, 11-10-2007. (b) Adult of Phymaturus agilis foraging and feeding on a Lycium gillesianum shrub. Photo: Cynthia González, 26-11-2006.
FIGURE 1 in When starvation challenges the tradition of niche conservatism: On a new species of the saxicolous genus Phymaturus from Patagonia Argentina with pseudoarboreal foraging behaviour (Iguania, Liolaemidae)
FIGURE 1. (a) Adult male holotype of Phymaturus agilis in life from Terra Typica, Río Negro, dorsal view. Photo: J.A. Scolaro, 10-03-2006. (b) Adult female paratype of Phymaturus agilis in life from Terra Typica, Río Negro, dorsal view. Photo: J.A. Scolaro, 10-03-2006.
Fig. 1 in Molecular phylogeny of Geoplaninae (Platyhelminthes) challenges current classification: proposal of taxonomic actions
Fig. 1 Scheme of the phylogenetic relationships of the main groups of land planarians after Álvarez-Presas et al. (2008) using Sluys et al. (2009) nomenclature.
Fig. 3 in Molecular phylogeny of Geoplaninae (Platyhelminthes) challenges current classification: proposal of taxonomic actions
Fig. 3 Phylogenetic tree inferred from the concatenated data set by maximum likelihood (ML). Squares at nodes indicate the presence and/ or level of support of the node under the different parameter schemes as sketched in the legend; black indicates maximum support. Symbols over the nodes denote ≥ 95% bootstrap support obtained in the ML (bluish triangles) and maximum posterior probability values (1) in Bayesian inference analyses (orangish circles). Numbers following species names refer to the accession numbers of the specimens deposited in the section Platyhelminthes of the MZUSP (MZUSP PL). The notation p indicates the species pictured on the right side (pictures not to scale). Scale bar is given in number of substitutions per site.
Fig. 1 in Molecular phylogeny of Geoplaninae (Platyhelminthes) challenges current classification: proposal of taxonomic actions
Fig. 1 Scheme of the phylogenetic relationships of the main groups of land planarians after Álvarez-Presas et al. (2008) using Sluys et al. (2009) nomenclature.
Artifact of the paper "An Empirical Investigation on the Challenges in Scientific Workflow Systems Development"
<p><strong><span>Scientific Workflow Systems (SWSs)</span></strong><span> play a critical role in the contemporary scientific landscape, significantly enriching research endeavors by augmenting productivity and fostering collaboration</span><span>, SWSs</span><span> elevate the standard of scholarly inquiry, fortifying its pillars of reproducibility and ethical adherence. Essentially, they </span><span>serve as</span><span> the bedrock upon which efficient, transparent, and impactful research </span><span>is built</span><span>, propelling knowledge and innovation across diverse fields. SWSs accomplish mundane yet essential tasks intrinsic to scientific inquiry—ranging from data acquisition to analysis and reporting. By liberating researchers from the shackles of manual labor, SWSs enable them to channel their energies toward more intellectually demanding pursuits, thereby enhancing the pace and quality of research outcomes. Moreover, SWSs wield a formidable influence in standardizing workflows across research cohorts, instilling a sense of uniformity in experimental methodologies and data-handling practices. This standardization not only cultivates a culture of rigor and coherence but also fosters cross-disciplinary dialogue and collaboration.</span></p> <p>Integral to the operation of SWSs is their capacity to integrate diverse tools, software, and data sources, effectively functioning as centralized hubs for research management. This integration expedites the research process and facilitates seamless data exchange and interoperability—a pivotal asset in an era characterized by the deluge of data and the imperative of interdisciplinary collaboration. Furthermore, SWSs afford researchers and project managers real-time insights into the progress of research endeavors, empowering them to identify bottlenecks, allocate resources judiciously, and optimize workflow execution. This granular oversight enhances project transparency and accountability and serves as a catalyst for informed decision-making.</p> <p>Crucially, SWSs are engineered to accommodate the complexities inherent in scientific inquiry, adeptly handling vast volumes of data and supporting parallel processing to meet the evolving demands of research projects. This scalability underscores their adaptability to diverse research paradigms, ensuring their relevance across a spectrum of scientific disciplines. Facilitating collaboration across geographic and temporal divides, SWSs offer a suite of collaborative features—including version control, shared workspaces, and communication tools—that transcend the constraints of physical proximity. By fostering a culture of inclusivity and knowledge exchange, SWSs catalyze innovation and synergy among distributed research teams.</p> <p>Moreover, SWSs serve as custodians of reproducibility, meticulously documenting each facet of the research workflow—from data sources to analysis methods—thus safeguarding the integrity of scientific inquiry. This commitment to transparency and methodological rigor underpins the credibility of research findings, engendering trust within the scientific community and beyond. The customizable nature of SWSs empowers research teams to tailor their workflows to suit their unique needs and preferences, further amplifying their utility and versatility. In essence, SWSs emerge not merely as tools of convenience but as indispensable allies in the relentless pursuit of scientific excellence.</p> <p>Numerous developers actively participate in the advancement of SWSs through diverse roles, including designing system architectures to ensure flexibility and performance, developing algorithms for data processing and analysis, crafting user-friendly interfaces, handling backend logic, integrating with external tools, and ensuring quality, security, and compliance. They address challenges such as optimizing performance and scalability by leveraging parallel processing and distributed computing techniques. To tackle these diverse tasks, developers encounter numerous challenges, often turning to crowd-sourced platforms like Stack Overflow and GitHub to discuss and address them. Stack Overflow serves as a vital resource for developers to seek solutions, learn new technologies, validate best practices, and engage with the programming community. Similarly, GitHub facilitates collaborative development by allowing developers to report problems, propose enhancements, and contribute to open-source projects. Our research draws insights from Stack Overflow discussions, GitHub issues, and pull request reports related to SWSs, reflecting the dynamic and collaborative nature of software development in this domain.</p>
Model and observational data for Morrison et al. (2020) JAMES paper entitled "Confronting the challenge of modeling cloud and precipitation microphysics"
This dataset was used to generate plots for a paper conditionally accepted in the Journal of Advances in Modeling Earth Systems (JAMES), an AGU journal. It comprises four different data sources: WRF simulations, radar observations, an idealized steady-state column model called the Bayesian Observationally-Constrained Statistical Scheme (BOSS), and idealized column rainshaft model using bin microphysics. The WRF simulations consist of output from a set of 11 runs using either bulk or bin microphysics schemes. The version of WRF is V3.9.1. To limit storage requirements, we have only saved a single time-slice of output at hour 6 of the simulations, along with the namelist.input file to generate these runs. The observational data are gridded and rotated composite NEXRAD reflectivity measurements from central Oklahoma. BOSS model output consists of microphysical parameter probability density functions and profiles of (as described in the meta-file for these data). The idealized bin microphysics rainshaft model output consists of profiles of drop mean size, reflectivity factor, precipitation rate, and drop concentration at two different time slices (described in the meta-data file for these data). We are requesting these data be added to the managed NCAR repository so they may be accessible to the community per AGU publication policy and in accordance with a U.S. DOE grant that partially supported this work.
Ionosphere-Thermosphere Data Published in "Challenges to Understanding the Earth's Ionosphere and Thermosphere"
<p>These data sets accompany an AGU Centennial publication "Challenges to Understanding the Earth's Ionosphere and Thermosphere" by R.A. Heelis and A. Maute. The data file tiegcm_totTgcm.s_181_12UT_GCpaper.nc is output from a thermosphere-ionosphere-electrodynamics general circulation model (TIEGCM) simulation described in the publication (DOI:10.1007/s11214-017-0330-3). The simulation is for 2009 solar minimum conditions. The ionospheric current and neutral wind in file "heelis_empirical_f130_12UT_extracted.nc" is based on a stand-alone dynamo model (described in doi:10.1002/2017JA024841) driven by the Horizontal Wind Model and ionospheric conductivities based on empirical models of the ionosphere (IRI) and the neutral atmosphere (MSIS). The calculation is for solar medium condition (F10.7=130) and DOY 181 under geomagnetic quiescent times.</p>
Challenging Bug Prediction and Repair Models with Synthetic Bugs
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Challenges in providing multilingual information access
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The ULS23 Challenge Public Training Dataset Part 4
<p>This dataset contains part of the imaging data for the <a href="https://uls23.grand-challenge.org/">Universal Lesion Segmentation Challenge (ULS23).</a> It contains lesion volumes-of-interest (VOI's) for part of the weakly annotated DeepLesion data. The annotations are made available through the <a href="https://github.com/MJJdG/ULS23">Challenge repository on GitHub</a>.<br><br>The Universal Lesion Segmentation 2023 (ULS23) data is licensed under CC BY-NC-SA 4.0 </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.