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Fig. 12 in The entomofauna of the Lower Permian fossil insect beds of Kansas and Oklahoma, USA
Fig. 12. Frequency distribution of adult-specimen abundance in 10-specimen size classes for the Elmo entomofauna.
Fig. 11 in The entomofauna of the Lower Permian fossil insect beds of Kansas and Oklahoma, USA
Fig. 11. Abundance of adult specimens of Elmo entomofauna. Values are plotted from species with fewest known specimens to species with the most known specimens. The species with the greatest numbers of specimens are identified.All labelled taxa are Grylloblattida except for D. tinctum (Psocoptera).
Fig. 10 in The entomofauna of the Lower Permian fossil insect beds of Kansas and Oklahoma, USA
Fig. 10. Size distributions of the Wellington Formation entomofauna by major taxonomic groups. Size is based on average forewing length in mm.
Fig. 8 in The entomofauna of the Lower Permian fossil insect beds of Kansas and Oklahoma, USA
Fig. 8. Distribution of size in the Wellington Formation entomofauna. Size is based on average forewing length in mm, plotted from smallest species to largest species. The 13 largest (forewing length>100 mm) and 2 smallest (forewing length <2 mm) species are identified. The two smallest species are both in the family Lophioneuridae. Ten of the largest species belong to the order Protodonata; M. grandis, to Palaeodictyoptera; G. carpenteri, to Caloneurodea; and S. ingens, to Megasecoptera.
Fig. 9 in The entomofauna of the Lower Permian fossil insect beds of Kansas and Oklahoma, USA
Fig. 9. Frequency distribution in 5-mm size classes of average forewing lengths for the Wellington Formation entomofauna.
Fig. 7 in The entomofauna of the Lower Permian fossil insect beds of Kansas and Oklahoma, USA
Fig. 7. Species diversity of the Wellington Formation entomofauna by Order (Suborder for the Grylloblattida). Orders are placed in three groups, Apterygota, Pterygota: Palaeoptera, and Pterygota: Neoptera, and are arranged in decreasing order of number of species. Bars show the number of species in each taxon unique to Elmo, unique to Midco and common to both. The nearly 200 species represent some 21 orders, 53 families and 106 genera.
Fig. 5 in The entomofauna of the Lower Permian fossil insect beds of Kansas and Oklahoma, USA
Fig. 5. Proportion of Wellington Formation entomofauna Neoptera and Palaeoptera subgroups unique to or shared between the Elmo and Midco localities, illustrating that the greater percentage of unique fauna at Elmo arises from the Neoptera. This reflects the fact that no review of the Neoptera from Midco has yet been published.
Fig. 6 in The entomofauna of the Lower Permian fossil insect beds of Kansas and Oklahoma, USA
Fig. 6. Comparison of the distribution of the Elmo Permian and World extant entomofaunas by major groups: Apterygota, Palaeoptera, Polyneoptera, Paraneoptera, and Holometabola.
Fig. 4 in The entomofauna of the Lower Permian fossil insect beds of Kansas and Oklahoma, USA
Fig. 4. (A) Gilbert O. Raasch (1903–1999) discovered the Midco fossil insect beds and documented the stratigraphy of the Oklahoma Wellington Formation in his PhD Thesis (Raasch 1946) (photo courtesy University of Wisconsin-Madison); (B) Paul Tasch of Wichita State University studied the palaeolimnology of the Wellington Formation in Kansas and Oklahoma and discovered additional Wellington Formation fossil insect localities in the late 1950's and early 1960's (photo courtesy of Wichita State University Department of Special Collections, Wichita, Kansas).
Fig. 3 in The entomofauna of the Lower Permian fossil insect beds of Kansas and Oklahoma, USA
Fig. 3. The settlement of Elmo, Kansas, as it appeared in the early 1900's. The fossil beds, located a few miles southeast, were named after the town. (Photo courtesy of the Wichita State University Libraries Department of Special Collections.)
Fig. 1 in The entomofauna of the Lower Permian fossil insect beds of Kansas and Oklahoma, USA
Fig. 1. Location and extent of the North American mid-continent Lower Permian Wellington Formation insect fossil deposits. The formation runs north to south some 270 km along eight counties, with major sites at the northern (Elmo, Kansas) and southern (Midco, Oklahoma) extremities.
Sponge presence increases the diversity and abundance of fish and invertebrates in a subtropical seagrass bed
<p>Ecosystem engineers (e.g., seagrasses) can have profound effects on biodiversity and community structure. Sponges possess many traits (e.g., create complex structure) that suggest they may act as ecosystem engineers, but we know little about their relationships with the animal communities that inhabit seagrass beds. This study explored the effects of the marine sponge, <em>Ircinia</em> <em>felix</em>, on fish and invertebrate taxa richness, abundance, turnover, and community composition in a seagrass bed through a 1-year field experiment performed in The Bahamas. We recorded the fish and invertebrate communities present in 5 × 5 m plots with the addition of either a live sponge, a polypropylene sponge replica (structure), or no additional structure (control). Both taxonomic groups responded similarly to the addition of a live sponge. Taxa richness and abundance increased at least 4-fold over a year in the presence of <em>I</em>. <em>felix</em> but decreased 2-fold or more in the other plot types. Live sponge presence increased the number of new taxa gained by at least 24% and reduced the number of taxa lost compared to other plot types. Finally, we found that the fish community shifted in a more consistent direction in the presence of a live sponge than in the other plots. Our study shows that sponges can act as ecosystem engineers in subtropical seagrass systems; however, additional research is needed to determine the full extent and implications of their ecological effects.</p>
Inconel 718 two ways: Powder Bed Fusion vs. Directed Energy Deposition
<p>Raw data associated with a paper submission.<br> "Inconel 718 two ways: Powder Bed Fusion vs. Directed Energy Deposition" submitted to Additive Manufacturing Letters.</p> <p>Contained are all the raw data used in the analysis, as well as csv's of any data plotted in graphs.</p> <p>Raw coaxial images captured during printing of various processing parameters<br> Optical Micrographs from which melt pool size and PDAS were measured<br> EBSD scans (.crc and .oip) of all disucssed samples<br> Raw Hardness Data<br> Position/Power logs from the build</p>
Dataset corresponding to the publication: Friction and slip measured at the bed of an Antarctic ice stream
<p>The following data is included here, which comprises the data presented in the publication:<br> Hudson, T. S., Kufner, S., Brisbourne, A. M., Kendall, J., Smith, A. M., Arthern, R., & Alley, R. (2023). Friction and slip measured at the bed of an Antarctic ice stream. Nature Geoscience.</p> <p>Files included are:<br> all_icequakes_2018to2019.csv - A catalogue of the icequakes used in the study.<br> cluster_????.csv - csv files containing the results for each icequake, grouped by cluster (i.e. one csv file per cluster).<br> slip_rate_movie.mp4 - A movie of slip-rate for each icequake through time.</p> <p>Headers for the files are as follows:<br> ------------------------------------- all_icequakes_2018to2019.csv file -------------------------------------<br> origin_time - Origin time of icequake, in UTC time.<br> Mw - Moment magnitude.<br> Mw_err - Error in Mw.<br> fc - Corner frequency (Hz).<br> fc_err - Error in corner frequency.<br> Q - Quality factor (measure of attenuation).<br> Q_err - Error in Q.<br> inter-event_time_s - Inter-event time between this icequake and the previous icequake (s).<br> stress_drop - Stress-drop of icequake (Pa).<br> stress_drop_err - error in stress-drop.<br> lat - Latitude (decimal degrees).<br> lon - Longitude (decimal degrees).<br> x (km) - x grid location (km).<br> y (km) - y grid location (km).<br> z (km bsl) - Depth (km bsl).<br> x_err (km) - Error in x (km).<br> y_err (km) - Error in y (km).<br> z_err (km) - Error in z (km).</p> <p>------------------------------------- cluster_XXXX.csv file -------------------------------------<br> mean_Mw - Mean moment magnitude for icequake from all receivers.<br> mean_fc - Corner frequency for icequake from all receivers (Hz).<br> stdev_Mw - Standard deviation in Mw.<br> stdev_fc - Standard deviation in corner-frequency.<br> mean_Q - Mean Q for icequake from all receivers.<br> stdev_Q - Standard deviation in Q.<br> n_obs - Number of receivers used to calculate above means.<br> lat - Latitude of icequake.<br> lons - Longitude of icequake.<br> depths - Depth of icequake (km bsl).<br> x - x location of icequake (km).<br> y - y location of icequake (km).<br> zs - z location of icequake (km bsl).<br> origin_time - Origin time of icequake (UTC time).<br> origin_time_days - Origin time in days relative to 1st January 2019 (days).<br> inter-event_time_s - Inter-event time from previous to current event (s).<br> stress_drop - Stress drop (Pa)<br> uncertainty_stress_drop - Uncertainty in stress-drop.<br> M_0 - Seismic moment release (Nm).<br> uncertainty_M_0 - Uncertainty in M_0.<br> fault_radius_m - Fault radius, in metres.<br> uncertainty_fault_radius_m - Uncertainty in fault radius (m).<br> slip_ice_m - Slip in the ice (m).<br> slip_till_m - Slip in the till (m).<br> uncertainty_slip_ice_m - Uncertainty in slip_ice_m.<br> uncertainty_slip_till_m - Uncertainty in slip_till_m.<br> RSF_v/v_0 - v/v_0 calculated for the rate-and-state model.<br> RSF_sigma - Normal stress calculated for the rate-and-state model (Pa).<br> RSF_tau - Shear-stress calculated for the rate-and-state model (Pa).<br> uncertainty_RSF_v/v_0 - Uncertainty in RSF_v/v_0.<br> uncertainty_RSF_sigma - Uncertainty in RSF_sigma.<br> uncertainty_RSF_tau - Uncertainty in RSF_tau.<br> RSF_G* - Effective shear-modulus calculated for the rate-and-state model (Pa).<br> RSF_Gb - Bed shear modulus calculated for the rate-and-state model (Pa).<br> RSF_slip_m - Slip calculated for the rate-and-state model (Pa).<br> uncertainty_RSF_G* - Uncertainty in RSF_G*.<br> uncertainty_RSF_Gb - Uncertainty in RSF_Gb.<br> uncertainty_RSF_slip_m - Uncertainty in RSF_slip_m.</p>
Fig. 7 in The geological setting and palaeoenvironmental and palaeoecological reconstructions of the Upper Permian insect beds at Belmont, New South Wales, Australia
Fig. 7. Ichnofossils: (A) Chewed Glossopteris leaf margin, specimen BPl 3; (B) Probable insect eggs oviposited into a Phyllotheca stem, specimen AM F 122931; (C, D) Chewed Glossopteris leaf margins, specimens BPl 1 and BPl 2. Scale bars = 1 mm in Figs A, B and 1 cm in Figs C, D.
Fig. 6 in The geological setting and palaeoenvironmental and palaeoecological reconstructions of the Upper Permian insect beds at Belmont, New South Wales, Australia
Fig. 6. Fossil insects: (A) Permosyne pincombeae (Coleoptera), holotype, Australian Museum AM F 19778 (original no. 14 T), note the indent, indicating the presence of a schiza, an adaptation to life in the water; (B) Wing belonging to an insect closely related to Stenoviicia incerta (Homoptera), specimen no. AM F 12948 (original no. B 325); (C) Wing of an undescribed protopsyllid (?) (Homoptera), specimen no. B 87; (D) Permosyne mitchelli (Coleoptera), specimen no. AM F 41273 (original number K129). Scale bars = 1 mm.
Fig. 5 in The geological setting and palaeoenvironmental and palaeoecological reconstructions of the Upper Permian insect beds at Belmont, New South Wales, Australia
Fig. 5. An attempted reconstruction of the ecosystem of the Belmont coal swamp and overbank pools. The main component insect groups, conchostracans and plants preserved in the oryctocoenosis are illustrated here and assigned to definite, probable and possible feeding groups.
Fig. 9 in The geological setting and palaeoenvironmental and palaeoecological reconstructions of the Upper Permian insect beds at Belmont, New South Wales, Australia
Fig. 9. Depth of fossil insects at site 205. Most wings are found in a Lagerstätt at a depth of 2 cm within the seam.
Data Hospital Beds, Physicians, Nurses and Expenditure for 20 Latin American Countries from 1960 to 2022
<p>Long-term quantitative series for 20 Latin American countries, spanning from 1960 to 2020, on the number of hospital beds, physicians, nurses and healthcare expenditure.</p> <p>Matus-Lopez, M. and Fernández Pérez, P. 2023. "Transformations in Latin American Healthcare: A Retrospective Analysis of Hospital Beds, Medical Doctors, and Nurses from 1960 to 2022". <em>Journal of Evolutionary Studies in Business</em>.</p> <p>The information was extracted from official reports and cross-country databases. Official reports were available in digital format in the Institutional Repository for Information Sharing (IRIS) of Pan American Health Organization (PAHO). They were summary of four-year reports on Health Conditions in the Americas (PAHO 1962, 1966, 1970, 1974, 1978, 1982, 1986, 1990, 1994, 1998, 2002a), annual reports of Basic Indicators (PAHO 2002b, 2007, 2008, 2010, 2013), Health in South America (PAHO 2012) and Core Indicators (PAHO 2016). Databases were Open Data Portal of the Pan American Health Organization (PLISA) (PAHO 2023), Core Indicator Database provided directly by PAHO (PAHO 2022), Data Portal of National Health Workforce Accounts of the World Health Organization (NHWA) (WHO 2022), and the Global Health Expenditure Database of the World Health Organization (GHED) (WHO 2023).</p> <p>Serie 1. Hospital Beds per 1,000 inhabitants</p> <p>Serie 2. Physicians per 10,000 inhabitants</p> <p>Serie 3. Nurses per 10,000 inhabitants</p> <p>Serie 4. Government spending on health, per capita. Constant US dollars of 2020</p> <p>Cite as:</p> <p> </p> <p> </p>
Dataset for "Reactivation strategies for nucleation-inhibited catalyst beds in continuously operated gas-release reactions from liquids."
<p>This dataset contains the measurements that were used to create the diagrams in the manuscript "Reactivation strategies for nucleation-inhibited catalyst beds in continuously operated gas-release reactions from liquids."</p>
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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.