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6,025 results for “Science of science”
FIGURE 22 in The type specimens of bees (Hymenoptera, Apoidea) deposited in the Zoological Institute of the Russian Academy of Sciences, St. Petersburg. Contribution VIII. Family Andrenidae, subfamily Panurginae
FIGURE 22. Panurginus kropotkini Romankova & Astafurova, 2011. Holotype, female: a—habitus, lateral view and labels; b—head, frontal view; c—labrum, dorsal view; d—head and mesosoma, dorsal view; e—metasoma, dorsal view. Scale bar: 1 mm.
FIGURE 15 in The type specimens of bees (Hymenoptera, Apoidea) deposited in the Zoological Institute of the Russian Academy of Sciences, St. Petersburg. Contribution VIII. Family Andrenidae, subfamily Panurginae
FIGURE 15. Panurginus alpotanini Romankova & Astafurova, 2011. Holotype, male: a—habitus, lateral view and labels; b—head, frontal view; c—S6, dorsal view; d—labrum, dorsal view; e—head and mesosoma, dorsal view; f—metasoma, dorsal view. Scale bar: 1 mm.
FIGURE 28. Panurginus picipes Morawitz, 1889 in The type specimens of bees (Hymenoptera, Apoidea) deposited in the Zoological Institute of the Russian Academy of Sciences, St. Petersburg. Contribution VIII. Family Andrenidae, subfamily Panurginae
FIGURE 28. Panurginus picipes Morawitz, 1889. Holotype, male: a—habitus, lateral view and labels; b—head, frontal view; c—S6, dorsal view; d—labrum, dorsal view; e—head and mesosoma, dorsal view; f—metasoma, dorsal view. Scale bar: 1 mm.
FIGURE 14. Meliturgula arabica Popov, 1951 in The type specimens of bees (Hymenoptera, Apoidea) deposited in the Zoological Institute of the Russian Academy of Sciences, St. Petersburg. Contribution VIII. Family Andrenidae, subfamily Panurginae
FIGURE 14. Meliturgula arabica Popov, 1951. Holotype, male: a—habitus, lateral view and labels; b, e—head, dorsal view (b), frontal view (e); c— pygidial plate, dorsal view; d—scutum, dorsal view; f—metasoma, dorsal view. Scale bar: 1 mm.
FIGURE 13. Melitturga spinosa Morawitz, 1891 in The type specimens of bees (Hymenoptera, Apoidea) deposited in the Zoological Institute of the Russian Academy of Sciences, St. Petersburg. Contribution VIII. Family Andrenidae, subfamily Panurginae
FIGURE 13. Melitturga spinosa Morawitz, 1891. Holotype, male: a—habitus, lateral view and labels; b—head, frontal view; c—head and mesosoma, dorsal view; d—metasoma, dorsal view; e—S7–S8, ventral view. Scale bar: 1 mm.
FIGURE 25. Panurginus nigripes Morawitz, 1880 in The type specimens of bees (Hymenoptera, Apoidea) deposited in the Zoological Institute of the Russian Academy of Sciences, St. Petersburg. Contribution VIII. Family Andrenidae, subfamily Panurginae
FIGURE 25. Panurginus nigripes Morawitz, 1880. Holotype, male: a—habitus, lateral view and labels; b—head, frontal view; c—head and mesosoma, dorsal view; d—metasoma, dorsal view. Scale bar: 1 mm.
FIGURE 5. Camptopoeum rufiventre Morawitz, 1880 in The type specimens of bees (Hymenoptera, Apoidea) deposited in the Zoological Institute of the Russian Academy of Sciences, St. Petersburg. Contribution VIII. Family Andrenidae, subfamily Panurginae
FIGURE 5. Camptopoeum rufiventre Morawitz, 1880. Holotype, male: a—habitus, lateral view and labels; b—head, frontal view; c—labrum, dorsal view; d—metasoma, dorsal view; e—head and mesosoma, dorsal view. Scale bar: 1 mm.
FIGURE 24 in The type specimens of bees (Hymenoptera, Apoidea) deposited in the Zoological Institute of the Russian Academy of Sciences, St. Petersburg. Contribution VIII. Family Andrenidae, subfamily Panurginae
FIGURE 24. Panurginus muraviovi Romankova & Astafurova, 2011. Holotype, male: a—habitus, lateral view and labels; b—head, frontal view; c—S6, dorsal view; d—labrum, dorsal view; e—mesosoma, dorsal view; f—metasoma, dorsal view. Scale bar: 1 mm.
FUTURE Science Frontiers
<p>The Table S1-S6 are curated breakout notes from the NSF-funded FUTURE 2024 Workshop (March 26-28, 2024). During the workshop, the first day of discussions focused on <em>“Critical science questions that require seafloor sampling,” </em>where participants: (I) defined the important sample types/sampling environment of their research; (II) assessed how well this seafloor environment is currently sampled; (III) reviewed how sample repositories/databases are currently used; and, (IV) evaluated justifications for acquiring new samples. Each breakout session culminated with a discussion of (V) what important science questions could be addressed soon (5–10 years), with existing or forthcoming assets and technologies, versus (VI) what might take longer (10+ years) and/or require the development of new assets or technologies. These motivating topics fed into the second day of discussions, which focused on <em>“Aligning seafloor sampling technology with critical science questions.” </em>Groups were guided by a common set of prompts, including what current resources were essential to the participants’ research, and what were the greatest challenges they faced in recovering the materials needed. The participants also discussed whether they could acquire the materials needed to address their science questions given current US assets (Figure 1 in FUTURE 2024 PI-team, 2024, AGU Advances 2024AV001560), how sample repositories and databases could be optimized for science needs, and the justification for acquiring or developing new technologies.</p> <p> </p>
Code and Data for "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.
<p>This repository contains the code and data for the study of "Global Surface Eddy Mixing Ellipses: Spatio-temporal Variability and Machine Learning Prediction" By Jing et al. Submitted to Frontiers in Marine Science.</p> <p>Specifically, this repository contains the following items: </p> <p>(1) The codes needed for assessing the representation and prediction skills of Random Forest (RF), Convolutional Neural Network (CNN) and Spatial Transformer Networks (STN) models. </p> <p>(2) Original and normalized data to run these codes.</p> <p>(3) Code here is built on early work from our laboratory (Jaderberg et al., 2015; Guan et al., 2022; Zhang et al., 2023), though great modifications have been made tailored to our scientific question.</p> <p>[1] Jaderberg, M., Simonyan, K., Zisserman, A., et al. (2015). Spatial transformer networks. Advances in neural information processing systems, 28.</p> <p>[2] Guan, W., Chen, R., Zhang, H., Yang, Y., & Wei, H. (2022). Seasonal surface eddy mixing in the Kuroshio Extension: Estimation and machine learning prediction. Journal of Geophysical Research: Oceans, 127 (3), e2021JC017967.</p> <div>[3] Zhang, G., Chen, R., Li, X., Li, L., Wei, H., & Guan, W. (2023). Temporal variability of global surface eddy diffusivities: Estimates and machine learning prediction. Journal of Physical Oceanography, 53 (7), 1711–1730.</div>
D1.4 LITERATURE REVIEW ON SOCIAL NETWORK ANALYSIS RELATED TO TRUST IN SCIENCE
<p>This document constitutes a part of the D1.4 Social Network Analysis and includes the literature review that was conducted to investigate the methodologies used for addressing the topic of trust in science in Online Social Networks (OSNs). This review contains studies that have approached the topic of trust in science from different perspectives in OSNs examining both data from OSNs and suveys related to OSNs providing useful insights about the factors that influence public trust in science. Important findings are derived from the literature review that affect public’s trust in science, such as the political ideology, educational level, and cultural factors. Also, different methods of the studies are described such as the analysis of the text of the messages, the reactions of users, and deep learning techniques. The findings of the literature review are provided to the final document of D1.4 as they address the further analysis of the Task 1.4 Social Network Analysis</p>
Thermo-staat project citizen science indoor temperature and humidity measurements for researching heat stress in the Netherlands
<p>These datasets contains raw data collected within the Thermo-staat citizen science project per the full year.<br>The aim of the project was to get insight in heat stres and if this problem is subject to social inequality.<br>Measurements contain indoor temperature and humidity data. Sensors in the dataset are bound to different rooms in a home and have different periods of activity. Each sensor has metadata about the room/situation attached. The aim was to get most sensors active in the summer.<br>Measurements where not done at a constant frequency, depending on the connectivity sensors did send up to once every 20 seconds.</p> <p>More information on the project on the website of the <a href="https://thermo-staat.nl/">Thermo-staat project</a></p> <p>More infromation on the <a href="https://thermo-staat.nl/download">data</a> </p> <p>Live data collection <a href="https://thermo-staat.waag.org/api/status_all">status</a> </p>
Butterflies at porch lights: exploring nocturnal light visitation in butterflies using community science data
<p>Data and R code for manuscript.</p>
Supporting material for Petricca et al. (2024), "Gravity and Radio Science Investigation at the Moons of Uranus to Reveal Subsurface Oceans and Characterize Interior Structures", JGR: Planets
<p>This archive contains the supplementary material for the paper "Gravity and Radio Science Investigation at the Moons of Uranus to Reveal Subsurface Oceans and Characterize Interior Structures", JGR: Planets</p> <p>Content of the dataset:</p> <ol> <li>Synthetic gravity fields for Ariel and Titania generated in the study</li> <li>SPICE kernels of the trajectory of the Uranus Orbiter and Probe designed at JPL</li> </ol> <p> </p> <p>---------------------------------------------------------------</p> <p>Synthetic gravity fields</p> <p>---------------------------------------------------------------</p> <p>The gravity fields are generated following the procedures described in Section 2.1.2 of the main paper. The hydrosphere thickness is assumed to be 190 km and 220 km for Ariel and Titania, respectively. The ocean density is fixed at 1050 kg/m^3. The syntethic topography is generated with pyshtools (Wieczorek and Meschede, 2018). The label of the file indicates the amplitude of the topography of each interface (ice shell or ocean floor) and the maximum degree of the spherical harmonics expansion. The files are formatted according to the Spherical Harmonics ASCII Data Record (SHADR) standard.</p> <p>The header of each file contains: reference radius (km), GM (km^3 / s^2), uncertatinty in the GM (not used and set to zero), maximum degree <em>l </em>of the<em> </em>expansion, maximum order<em> m </em>of the expansion, normalization (0 for unnormalized, 1 for 4pi normalization), reference latitude, reference longitude</p> <p>The columns contain: degree <em>l</em>, order <em>m</em>, coefficient C_<em>lm</em>, coefficient S_<em>lm</em></p> <p>---------------------------------------------------------------</p> <p>UOP trajectories</p> <p>---------------------------------------------------------------</p> <p>The reference positions and velocities of the UOP were generated by Damon Landau (JPL) as part of an internal study at JPL. These initial positions and velocities were numerically integrated by Flavio Petricca (JPL) using the dynamical models described in the main paper. For this reason, the trajectories only cover +- 8 hours from closest approach with each moon and not the entire tour.</p> <p>The ID of the spacecraft is set to -999. The simple text kernel provided here (id_name_map.txt) can be loaded in the kernel pool to associate the ID code with the SPICE names 'URANUS ORBITER PROBE' and 'UOP' for a more explicit and user-friendly access to the trajectories.</p>
Open Science Website Review (Team 2)
<p>A document detailing Team 2's analysis of the various websites promoting the concept of open science for the "Open Science" Course and using Zenodo to generate a DOI for the aforementioned document</p>
The island biogeography of the eBird citizen-science program
Aim: Island biotas face an array of unique challenges under global change. Monitoring and research efforts, however, have been hindered by the large number of islands, their broad distribution and geographic isolation. Global citizen-science initiatives have the potential to address these deficiencies. Here, we determine how the eBird citizen-science program is currently sampling island bird assemblages annually and how these patterns are developing over time. Location: Global. Taxa: Birds. Methods: We compiled occurrence information of non-marine bird species across the world's islands (n = 21,813) over an 18-year period (2002-2019) from eBird. We estimated annual survey completeness and species richness across islands, which we examined in relation to six geographical and four climatic features. Results: eBird contained bird occurrence information for ca. 20% of the world's islands (n = 4,205) with ca. 8% classified as well surveyed annually (n =1,644). eBird participants tended to survey larger islands that were more distant from the mainland. These islands had lower proximity to other islands and contained a broader range of elevations. Temperature, precipitation, and temperature seasonality were at intermediate levels. Precipitation seasonality was at low and intermediate levels. Islands located between 10-60° N latitude and 30-40° S latitude were overrepresented, and islands located between 60-130° W longitude were underrepresented. From 2002 to 2019, the number of islands surveyed annually increased by ca. 96.3 islands/year. During this period, island size decreased, distance from mainland did not change, proximity to other islands increased, and elevation range decreased. Main conclusions: The eBird program tends to survey larger islands containing intermediate climates that are more isolated from the mainland and other islands. These findings provide a framework to support the rigorous application of eBird data in avian island biogeography. Our findings also emphasize citizen science as a resource to support ecological research, conservation, and monitoring efforts across remote regions of the globe.
Chimpanzee identification and social Network construction through an online citizen science platform
<p><span><span><span><span><span><span><span><span><span><span><span>Citizen science has grown rapidly in popularity in recent years due to its potential to educate and engage the public while providing a means to address a myriad of scientific questions. However, the rise in popularity of citizen science has also been accompanied by concerns about the quality of data emerging from citizen science research projects. We assessed data quality in the online citizen scientist platform Chimp&See, which hosts camera trap videos of chimpanzees (<i>Pan troglodytes</i>) and other species across Equatorial Africa. In particular, we compared detection and identification of individual chimpanzees by citizen scientists to that of experts with years of experience studying those chimpanzees. We found that citizen scientists typically detected the same number of individual chimpanzees as experts, but assigned far fewer identifications (IDs) to those individuals. Those IDs assigned, however, were nearly always in agreement with the IDs provided by experts. We applied the data sets of citizen scientists and experts by constructing social networks from each. We found that both social networks were relatively robust and shared a similar structure, as well as having positively correlated individual network positions. Our findings demonstrate that, although citizen scientists produced a smaller data set based on fewer confirmed IDs, the data strongly reflect expert classifications and can be used for meaningful assessments of group structure and dynamics. This approach expands opportunities for social research and conservation monitoring in great apes and many other individually identifiable species. </span></span></span></span></span></span></span></span></span></span></span></p>
Data for Matsala et al. (2021) paper in Applied Vegetation Science journal
<p>Data to run models described in Matsala et al. (2021) paper in Applied Vegetation Science journal.</p>
Open Science Team 7 - Survey Raw Data
<p>Raw Data files (.csv and .xlxs) from Team 7's survey. All responses have been anonymized</p>
FIGURE. Mucuna pruriens (L.) DC. var. hirsuta (Wight & Arn.) Wilmot-Dear. A. Holotype of M. pruriens (L.) DC. var. hirsuta (Wight & Arn.) Wilmot-Dear (R. Wight 750, K000797547, © Royal Botanic Gardens, Kew). B. Isotype of M. pruriens (L.) DC. var. hirsuta (Wight & Arn.) Wilmot-Dear (R. Wight 750, E00174529, © Royal Botanic Garden, Edinburgh). C. Holotype of M. incurvata Wilmot-Dear & R. Sa (C. W. Wang 79571, PE00414137, © Institute of Botany, Chinese Academy of Sciences). D. Isotype of M. incurvata Wilmot-Dear & R. Sa (C. W. Wang 79571, A00195002, © Harvard University). E. S. Mokim s. n. (L4306572) from Kachin Hills, Myanmar [Upper Burma], © Naturalis Biodiversity Center. F. China-Vietnam Joint Exped. 1441 (PE00416831) from Vietnam, © Institute of Botany, Chinese Academy of Sciences. G. X. X. Guo 1401 (CSH0142751) from Jinghong, Yunnan, China, © Shanghai Chenshan Herbarium. H. K. W. Jiang SSPN13 (CSH0160977) from Mengla, Yunnan, China, © Shanghai Chenshan Herbarium. I. K. M. Feng 5399 (KUN0618388) from Hekou, Yunnan, China, © Kunming Institute of Botany, CAS. in Legume additions to the flora of China
FIGURE. Mucuna pruriens (L.) DC. var. hirsuta (Wight & Arn.) Wilmot-Dear. A. Holotype of M. pruriens (L.) DC. var. hirsuta (Wight & Arn.) Wilmot-Dear (R. Wight 750, K000797547, © Royal Botanic Gardens, Kew). B. Isotype of M. pruriens (L.) DC. var. hirsuta (Wight & Arn.) Wilmot-Dear (R. Wight 750, E00174529, © Royal Botanic Garden, Edinburgh). C. Holotype of M. incurvata Wilmot-Dear & R. Sa (C. W. Wang 79571, PE00414137, © Institute of Botany, Chinese Academy of Sciences). D. Isotype of M. incurvata Wilmot-Dear & R. Sa (C. W. Wang 79571, A00195002, © Harvard University). E. S. Mokim s. n. (L4306572) from Kachin Hills, Myanmar [Upper Burma], © Naturalis Biodiversity Center. F. China-Vietnam Joint Exped. 1441 (PE00416831) from Vietnam, © Institute of Botany, Chinese Academy of Sciences. G. X. X. Guo 1401 (CSH0142751) from Jinghong, Yunnan, China, © Shanghai Chenshan Herbarium. H. K. W. Jiang SSPN13 (CSH0160977) from Mengla, Yunnan, China, © Shanghai Chenshan Herbarium. I. K. M. Feng 5399 (KUN0618388) from Hekou, Yunnan, China, © Kunming Institute of Botany, CAS.
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.