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6,025 results for “Science of science”
FIGURE 14 in The type material of spider wasps (Hymenoptera, Pompilidae) deposited in the Federal Scientific Center of the East Asia Terrestrial Biodiversity, Russian Academy of Sciences, Vladivostok, Russia
FIGURE 14. Arachnospila rasnitsyni Loktionov & Lelej, 2011, ♂, holotype. A. Labels. B. Habitus, lateral view. C. Head, dorsal view. D. Head, lateral view. E. Head, frontal view. F. Hypopygium, ventral view. G. Hypopygium, lateral view. H. Genitalia, ventral view. Scale bar: 1.0 mm for B; 0.5 mm for F, G, H; 0.2 mm for C, D, E. F, H, G from Loktionov & Lelej 2011.
FIGURE 20 in The type material of spider wasps (Hymenoptera, Pompilidae) deposited in the Federal Scientific Center of the East Asia Terrestrial Biodiversity, Russian Academy of Sciences, Vladivostok, Russia
FIGURE 20. Auplopus mama Loktionov & Lelej, 2014, ♂, holotype. A. Labels. B. Habitus, lateral view. C. Head, lateral view. D. Head, dorsal view. E. Head, frontal view. F. Hypopygium, ventral view. G. Genitalia, ventral view. Scale bar: 1.0 mm for B; 0.5 mm for F, G; 0.2 mm for C, D, E. F, G from Loktionov & Lelej 2014.
FIGURE 1. Agenioideus pacificus Lelej, 1994 in The type material of spider wasps (Hymenoptera, Pompilidae) deposited in the Federal Scientific Center of the East Asia Terrestrial Biodiversity, Russian Academy of Sciences, Vladivostok, Russia
FIGURE 1. Agenioideus pacificus Lelej, 1994, ♂, holotype. A. Labels. B. Habitus, lateral view. C. Head, dorsal view. D. Head, lateral view. E. Head, frontal view. F. Hypopygium and sternum 7, ventral view. G. Genitalia, ventral view. Scale bar: 0.5 mm for B, F, G; 0.1 mm for C, D, E. F and G from Loktionov & Lelej 2014.
FIGURE 28 in The type material of spider wasps (Hymenoptera, Pompilidae) deposited in the Federal Scientific Center of the East Asia Terrestrial Biodiversity, Russian Academy of Sciences, Vladivostok, Russia
FIGURE 28. Stigmaporus volgadon Loktionov & Lelej, 2016, ♂, holotype. A. Labels. B. Habitus, lateral view. C. Head, lateral view. D. Head, dorsal view. E. Head, frontal view. F. Hypopygium, ventral view. G. Genitalia, ventral view. Scale bar: 1.0 mm for B; 0.2 mm for C, D, E, F, G. F, G from Loktionov et al. 2016.
FIGURE 24 in The type material of spider wasps (Hymenoptera, Pompilidae) deposited in the Federal Scientific Center of the East Asia Terrestrial Biodiversity, Russian Academy of Sciences, Vladivostok, Russia
FIGURE 24. Evagetes orientalis Lelej & Loktionov, 2009, ♂, holotype. A. Labels. B. Habitus, lateral view. C. Head, lateral view. D. Head, dorsal view. E. Head, frontal view. F. Hypopygium and sternum 7, ventral view. G. Genitalia, ventral view. Scale bar: 1.0 mm for B; 0.5 mm for F, G; 0.2 mm for C, D, E. F, G from Loktionov & Lelej 2014.
FIGURE 13 in The type material of spider wasps (Hymenoptera, Pompilidae) deposited in the Federal Scientific Center of the East Asia Terrestrial Biodiversity, Russian Academy of Sciences, Vladivostok, Russia
FIGURE 13. Arachnospila orientausa Loktionov & Lelej, 2011, ♂, holotype. A. Labels. B. Habitus, lateral view. C. Head, dorsal view. D. Head, lateral view. E. Head, frontal view. F. Hypopygium and sternum 7, ventral view. G. Hypopygium, lateral view. H. Genitalia, ventral view. Scale bar: 1.0 mm for B; 0.5 mm for F, G, H; 0.2 mm for C, D, E. F, H, G from Loktionov & Lelej 2011.
FIGURE 25 in The type material of spider wasps (Hymenoptera, Pompilidae) deposited in the Federal Scientific Center of the East Asia Terrestrial Biodiversity, Russian Academy of Sciences, Vladivostok, Russia
FIGURE 25. Kuriloagenia ermolenkoi Loktionov & Lelej, 2014, ♂, holotype. A. Labels. B. Habitus, lateral view. C. Head, lateral view. D. Head, dorsal view. E. Head, frontal view. F. Hypopygium and sternum 7, ventral view. G. Genitalia, ventral view. Scale bar: 1.0 mm for B; 0.5 mm for F, G; 0.2 mm for C, D, E. F, G from Loktionov & Lelej 2014.
Altmetric data of the documents from Web of Science with the affiliation to the Czech Republic
<p>These datasets were generated for the master thesis with the title "Altmetrics and its use in the evaluation of scientific research" (in Czech "Altmetrie a její využití při hodnocení vědeckého výzkumu"), that was submitted in 2022 at the Institute Information Studies and Librarianship, Charles University, Prague, Czech Republic. There are 3 files, where the first one "Příloha_číslo_1-processed_data.xlsx" contains processed and analyzed data from the other two. "Příloha_číslo_2-data_altmetrics_Kvet_1st_download.csv" is the file with raw altmetric data downloaded on the 9/1/2022. "Příloha_číslo_3-data_altmetrics_Kvet_2st_download.csv" are the same data but downloaded two months later to see the difference.</p> <p>Altmetric data came from PlumX and Altmetric.com aggregators. Before publishing them here there were completely anonymized so there is no possibility to assign them to particular research papers. Dataset contains altmetric data of the research documents published in the period from 2017 to 2021. All documents have affiliation to the Czech Republic.</p> <p>The tool for collection and analyzation is available on Github here: <a href="https://github.com/kvetjo/Altmetrics_analyze_tool">https://github.com/kvetjo/Altmetrics_analyze_tool</a></p> <p>Thesis reference in Czech:</p> <p>KVĚT, Jonáš. <em>Altmetrie a její využití při hodnocení vědeckého výzkumu</em> [online]. Praha, 2022 [cit. 2022-05-03]. Diplomová práce. Univerzita Karlova. Filozofická fakulta. Ústav informačních studií a knihovnictví. Vedoucí práce Jan Dvořák.</p>
Spatial clustering of trumpetfish shadowing behaviour in the Caribbean Sea revealed by citizen science
<p>The West Atlantic trumpetfish (Aulostomus maculatus) performs an unusual hunting strategy, termed shadowing, whereby a trumpetfish swims closely behind or next to another 'host' species to facilitate the capture of prey. Despite trumpetfish being observed throughout the Caribbean, observations of this behaviour appear to be concentrated to a handful of localities. Here we assess the degree of geographical clustering of shadowing behaviour throughout the Caribbean Sea, and identify ecological features associated with the likelihood of its occurrence. To do this, we used a citizen science approach by creating and distributing an online survey to target frequent divers across this region. While the vast majority of participants observed trumpetfish on nearly every dive across the Caribbean, using random labelling spatial analyses, we found the frequency of shadowing behaviour was geographically clustered; participants that were within ~ 120 km of each other reported observations of shadowing that were more similar than would be expected by chance. Our survey also highlighted that trumpetfish were more likely to be observed shadowing than observed alone in a particular habitat type, and with particular host species, suggesting potential ecological factors that could drive the uneven distribution of this behaviour. Our results demonstrate that this behavioural hunting strategy is spatially clustered and, more generally, highlight the power of using citizen science to investigate variation in animal behaviour over thousands of square kilometres.</p>
Data from: Identifying conservation priorities for gorgonian forests in Italian coastal waters with multiple methods including citizen science and social media content analysis
<div> <div> <div> <div> <p>Gorgonian forests are among the most complex of subtidal habitats in the Mediterranean Sea, supporting high biodiversity and providing diverse ecosystem services. Despite their iconic status, the geographical distribution and condition of gorgonian species is poorly known. Using multiple online data sources, our primary aims were to compile, map and analyse observations of gorgonian forests in Italian coastal waters to assess the biological complexity of gorgonian forests; evaluate impacts and vulnerable species, and identify areas of special interest inside and outside of existing MPAs to help prioritise conservation strategies and actions.</p> </div> </div> </div> </div>
Open Science Capacity Building in Africa
<p>List about Open Science Capacity Building stakeholders in Africa that were shared during the <a href="https://events.unesco.org/event?id=4075814764&lang=1033">First meeting of the Working Group on Open Science Capacity Building</a> hosted by UNESCO.</p>
[Suplemental Materials] Accuracy of bird identifications in citizen science data: a quantification of errors using photographic records
<p><strong>Supporting Information</strong></p> <p><strong>Appendix S1</strong>. The full dataset used in this study with a reproducible R code to perform data quality and network analyses. </p> <p><strong>Appendix S2</strong>. All species ranking of identification accuracy of photo reports submitted to eBird in Argentina. The ranking is first ordered by the minimum value found for either precision and recall scores, and second by the number of samples analyzed for each species. Species that were tagged as difficult to identify are indicated as ‘TRUE’ in column D named ‘hard_to_id’.</p> <p><strong>Appendix S3</strong>. High-resolution network (Html file).</p>
Accuracy of bird identifications in citizen science data: a quantification of errors using photographic records [R code]
<p><strong>Appendix S1</strong>. The full dataset used in this study with a reproducible R code to perform data quality and network analyses. R code archived to Zenodo for publication. </p>
Book of Abstracts from Conference for the Society for the Preservation of Natural History Collections (SPNHC), International Partner – BHL (Biodiversity Heritage Library) and National Partner – NatSCA (Natural Sciences Collections Association) 2022
<p>The complete book of abstracts (Posters & Oral Presentations). </p>
Analytics of collaborations and performance of citizen science teams during the GEAR cycle 2 of the Crowd4SDG project.
<p>This data frame is part of the deliverable 4.2 of the Crowd4SDG project.</p> <p>It contains the measured analytics of citizen collaborations using new metrics/descriptors developed during the first year of the Crowd4SDG project. </p> <pre>The goal of the Crowd4SDG project is to research the extent to which Citizen Science (CS) can provide an essential source of non-traditional data for tracking progress towards the SDGs, as well as the ability of CS to generate social innovations that enable such progress. In the Crowd4SDG project, the Work Package 4 aims to develop and monitor new metrics and develop statistical models of team engagement and collaboration that contribute to the many-faceted outcomes of the citizen science projects developed within the Crowd4SDG consortium over the 3-years course of the project. Here, we share a dataframe of features collected during the GEAR cycle 2 pertaining to team composition, activity, performance and interaction dynamics. In particular, we leveraged the CoSo platform for collecting self-reported data on collaborations and task allocation structure of participating teams, as well as Slack data for measuring communication networks. The related findings are presented in the deliverable 4.4 of the Crowd4SDG project and serve as a basis for i) exhibiting the potential of using digital traces to derive measures related to team process, ii) highlighting perspectives for monitoring metrics in the next GEAR cycle.</pre>
Smell Pittsburgh: Engaging Community Citizen Science for Air Quality
<p>Link to the files and description of the Smell Pittsburgh Dataset –<br> <a href="https://eur04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2FCMU-CREATE-Lab%2Fsmell-pittsburgh-prediction%2Ftree%2Fmaster%2Fdataset%2Fv2&data=05%7C01%7Cy.c.hsu%40uva.nl%7C89562067341d40d0bad308da2c475652%7Ca0f1cacd618c4403b94576fb3d6874e5%7C0%7C0%7C637870982141190827%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=gWn0nGRUl5EAHvDfJwTlTNJN%2BWFH62NX6Mw%2B2web6XE%3D&reserved=0">https://github.com/CMU-CREATE-Lab/smell-pittsburgh-prediction/tree/master/dataset/v2</a></p> <p>Smell Pittsburgh (<a href="https://smellpgh.org">https://smellpgh.org</a>) is a mobile application for crowdsourcing reports of bad odors, such as those generated from air pollution. The data is used to train a machine learning model to predict the presence of bad smell and create push notifications to inform citizens about the bad smell. The motivation, background, and design of the Smell Pittsburgh application is described in the following paper.</p> <ul> <li>Yen-Chia Hsu, Jennifer Cross, Paul Dille, Michael Tasota, Beatrice Dias, Randy Sargent, Ting-Hao (Kenneth) Huang, and Illah Nourbakhsh. 2020. Smell Pittsburgh: Engaging Community Citizen Science for Air Quality. ACM Transactions on Interactive Intelligent Systems. 10, 4, Article 32. DOI:<a href="https://doi.org/10.1145/3369397">https://doi.org/10.1145/3369397</a>. Preprint:<a href="https://arxiv.org/pdf/1912.11936.pdf">https://arxiv.org/pdf/1912.11936.pdf</a>.</li> </ul>
Data from: Combining citizen science species distribution models and stable isotopes reveals migratory connectivity in the secretive Virginia rail
Stable hydrogen isotope (δD) methods for tracking animal movement are widely used yet often produce low resolution assignments. Incorporating prior knowledge of abundance, distribution or movement patterns can ameliorate this limitation, but data are lacking for most species. We demonstrate how observations reported by citizen scientists can be used to develop robust estimates of species distributions and to constrain δD assignments. We developed a Bayesian framework to refine isotopic estimates of migrant animal origins conditional on species distribution models constructed from citizen scientist observations. To illustrate this approach, we analysed the migratory connectivity of the Virginia rail Rallus limicola, a secretive and declining migratory game bird in North America. Citizen science observations enabled both estimation of sampling bias and construction of bias-corrected species distribution models. Conditioning δD assignments on these species distribution models yielded comparably high-resolution assignments. Most Virginia rails wintering across five Gulf Coast sites spent the previous summer near the Great Lakes, although a considerable minority originated from the Chesapeake Bay watershed or Prairie Pothole region of North Dakota. Conversely, the majority of migrating Virginia rails from a site in the Great Lakes most likely spent the previous winter on the Gulf Coast between Texas and Louisiana. Synthesis and applications. In this analysis, Virginia rail migratory connectivity does not fully correspond to the administrative flyways used to manage migratory birds. This example demonstrates that with the increasing availability of citizen science data to create species distribution models, our framework can produce high-resolution estimates of migratory connectivity for many animals, including cryptic species. Empirical evidence of links between seasonal habitats will help enable effective habitat management, hunting quotas and population monitoring and also highlight critical knowledge gaps.
FIGURES 66–69 in A catalogue of the types of rove beetles (Coleoptera: Staphylinidae) deposited in the collection of Johan Heinrich Hochhuth in the National Museum of Natural History of the National Academy of Sciences of Ukraine
FIGURES 66–69. Philonthus rutilipennis (Figs 66a–d), Aleochara solida (Figs 67a–b), Philonthus subopacus (Figs 68a–d), Myrmedonia subtumida (Figs 69a–f): 66a, 67a, 68a, 69a—habitus, 66b, 68c, 69e—aedeagus, 68b—left antennomere, 66c, 68d—apical abdominal segments, 66d, 67b, 68d, 69b, 69f–labels. Scale bars: 1.0 mm (Figs 66a, 68a, 69a, 68c), 0.2 mm (Figs 66b–c, 68b–c, 68d–e).
FIGURES 70–73 in A catalogue of the types of rove beetles (Coleoptera: Staphylinidae) deposited in the collection of Johan Heinrich Hochhuth in the National Museum of Natural History of the National Academy of Sciences of Ukraine
FIGURES 70–73. Brachyusa concolor (Figs 70a–e), Philonthus tanaicus (Figs 71a–b), Trogophloeus tarsalis (Figs 72a–c), Philonthus transbaicalia (Figs 73a–f): 70a, 70d, 71a, 72a, 73a, 73e—habitus, 70b, 73c—aedeagus, 72b—hind leg and apical abdominal segments, 73b—apical abdominal segment, 70c, 70e, 71b, 72c, 73d, 73f—labels. Scale bars: 1.0 mm (Figs. 70a, 70d, 71a, 72a, 73a, 73e), 0.2 mm (Figs. 70b, 72b, 73b–c).
FIGURES 52–55 in A catalogue of the types of rove beetles (Coleoptera: Staphylinidae) deposited in the collection of Johan Heinrich Hochhuth in the National Museum of Natural History of the National Academy of Sciences of Ukraine
FIGURES 52–55. Stenus minutus (Figs 52a–b), Aleochara notatipennis (Figs 53a–c), Lathrobium pallidipenne (Figs 54a–d), Ocypus philonthoides (Figs 55a–c): 52a–b, 53a, 54a, 55a—habitus, 53b, 54b—aedeagus, lateral view, 54c—apical abdominal segment, 55b–antennomeres, apical part of abdomen and aedeagus, 53c, 54d, 55c—labels. Scale bars: 2.0 mm (55a), 1.0 mm (Figs 52a–b, 53a, 54a), 0.5 mm (Figs 53b, 54b–c).
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.