Find research datasets worth reusing

Figure 7 in Taxonomic notes on the Asian frogs of the tribe Paini (Ranidae, Dicroglossinae): 1. Morphology and synonymy of Chaparana aenea (Smith, 1922), with proposal of a new statistical method for testing homogeneity of small samples

Figure 7. Chaparana aenea (Smith, 1922), MNHN 1999.5821, topotype of Rana (Chaparana) fansipani Bourret, 1939, adult male from Fan Si Pan, Vietnam: ventral view of chest and throat showing shape of patch of nuptial spines.

Figure 6 in Taxonomic notes on the Asian frogs of the tribe Paini (Ranidae, Dicroglossinae): 1. Morphology and synonymy of Chaparana aenea (Smith, 1922), with proposal of a new statistical method for testing homogeneity of small samples

Figure 6. Plots of factors 1 and 2 of principal component analysis based on varimax rotated coefficients for logtransposed characters (25 measurements) for four adult specimens of Chaparana aenea (Smith, 1922) (see Table II) and six adult specimens of Chaparana unculuanus (Liu, Hu and Yang, 1960) (see Table IV).

Figure 3 in Taxonomic notes on the Asian frogs of the tribe Paini (Ranidae, Dicroglossinae): 1. Morphology and synonymy of Chaparana aenea (Smith, 1922), with proposal of a new statistical method for testing homogeneity of small samples

Figure 3. Chaparana aenea (Smith, 1922). (a–c) MNHN 1989.0712, non-breeding adult male from Doi Inthanon, Chiangmai Province, Thailand: (a) whole body in dorsal view; (b) whole body in ventral view; (c) head in right lateral view. (d–f) MNHN 1948.0139, holotype of Rana (Chaparana) fansipani Bourret, 1939, juvenile male from Fan Si Pan, Vietnam: (d) whole body in dorsal view; (e) whole body in ventral view; (f) head in right lateral view.

Figure 4 in Taxonomic notes on the Asian frogs of the tribe Paini (Ranidae, Dicroglossinae): 1. Morphology and synonymy of Chaparana aenea (Smith, 1922), with proposal of a new statistical method for testing homogeneity of small samples

Figure 4. (a–c) Chaparana aenea (Smith, 1922), MNHN 1999.5818, topotype of Rana (Chaparana) fansipani Bourret, 1939, adult male from Fan Si Pan, Vietnam: (a) whole body in dorsal view; (b) whole body in ventral view; (c) head in right lateral view. (d–f) Chaparana unculuanus (Liu, Hu and Yang, 1960), MNHN 2001.0280, adult female from Jingdong Xian, Yunnan, China: (d) whole body in dorsal view; (e) whole body in ventral view; (f) head in right lateral view.

Figure 1 in Taxonomic notes on the Asian frogs of the tribe Paini (Ranidae, Dicroglossinae): 1. Morphology and synonymy of Chaparana aenea (Smith, 1922), with proposal of a new statistical method for testing homogeneity of small samples

Figure 1. Chaparana aenea (Smith, 1922). (a–c) BMNH 1947.2.2.31 (ex BMNH 1922.7.4.4, ex MAS 5821), holotype of Rana aenea Smith, 1922, juvenile female from Doi Chang, Thailand: (a) whole body in dorsal view; (b) whole body in ventral view; (c) head in right lateral view. (d–f) MNHN 1999.5821, topotype of Rana (Chaparana) fansipani Bourret, 1939, adult male from Fan Si Pan, Vietnam: (d) whole body in dorsal view; (e) whole body in ventral view; (f) head in right lateral view.

opencc-by-sa-4.0Aug 2021View details →

Sampled Encounters of Low Altitude Manned Aircraft

Summary Paired encounters between two manned aircraft based on samples from Bayesian dynamic models of aircraft behavior. The encounters are uncorrelated in the sense that it is assumed that air traffic control services are not being provided during the encounter. Description For many aviation safety studies, aircraft behavior is represented using encounter models, which are statistical models of how aircraft behave during close encounters. They are used to provide a realistic representation of the range of encounter flight dynamics where an aircraft collision avoidance system would be likely to alert. These models represent aircraft behavior (rates) during the course of the encounter with other aircraft. Encounter models have been developed for many different manned operational contexts. For more details on encounter models, please refer to the overview on the Airspace Encounter Models GitHub organization: <a href="https://github.com/Airspace-Encounter-Models/em-overview">https://github.com/Airspace-Encounter-Models/em-overview</a>. The Bayesian models were sourced from <a href="https://github.com/Airspace-Encounter-Models/em-model-manned-bayes/tree/v1.3">v1.3 of the em-model-manned-bayes </a>repository while<a href="https://github.com/Airspace-Encounter-Models/em-pairing-uncor-importancesampling/tree/v1.2"> v1.2 of em-pairing-uncor-importancesampling</a> contained the software to pair samples from the Bayesian models to create encounters. This dataset contains 1.6 million encounters across 18 different encounter sets, each consisting of 100,000 unique encounters. The encounters were generated in June 2021. Please refer to the software for details on the format and variables for each encounter. The dataset is provided as a single .zip archive with multiple directories. Simply download the .zip file and extract. An excel file with two sheets are document the configuration parameters. The first sheet reports the tradespace and configurations for each encounter set. The model files correspond to Bayesian models from em-model-manned-bayes, while tCPA and sample_time are encounter parameters from em-pairing-uncor-importancesampling. The tCPA variable the desired time at which the closest point of approach occurs and sample_time variable is the desired total duration of the encounter in seconds. The sample_time value must be greater than tCPA. The other sheet, fixed_paramters, documents the fixed encounter parameters. The minimum initial horizontal and vertical separation requirements were defined by R_min and H_min. The bin edges and desired proportions defined the importance sampling criteria for the horizontal miss distance (HMD) and vertical miss distance (VMD) at the closest point of approach. The encIds denote that 100,000 encounters were generated for each set and that the random seed (randSeed) was set to one. Distribution Statement DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited. © 2021 Massachusetts Institute of Technology. Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work. This material is based upon work supported by the Federal Aviation Administration under Air Force Contract No. FA8702-15-D-0001.  Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Federal Aviation Administration. This document is derived from work done for the FAA (and possibly others); it is not the direct product of work done for the FAA. The information provided herein may include content supplied by third parties.  Although the data and information contained herein has been produced or processed from sources believed to be reliable, the Federal Aviation Administration makes no warranty, expressed or implied, regarding the accuracy, adequacy, completeness, legality, reliability or usefulness of any information, conclusions or recommendations provided herein.  Distribution of the information contained herein does not constitute an endorsement or warranty of the data or information provided herein by the Federal Aviation Administration or the U.S. Department of Transportation.  Neither the Federal Aviation Administration nor the U.S. Department of Transportation shall be held liable for any improper or incorrect use of the information contained herein and assumes no responsibility for anyone’s use of the information.  The Federal Aviation Administration and U.S. Department of Transportation shall not be liable for any claim for any loss, harm, or other damages arising from access to or use of data or information, including without limitation any direct, indirect, incidental, exemplary, special or consequential damages, even if advised of the possibility of such damages.  The Federal Aviation Administration shall not be liable to anyone for any decision made or action taken, or not taken, in reliance on the information contained herein.

opencc-by-4.0May 2009View details →

Fig. 9. Protopanderodus gradatus Serpagli, 1974. All from sample WCB705 in Biogeographic and Biostratigraphic Implications of the Serratognathus bilobatus Fauna (Conodonta) from the Emanuel Formation (Early Ordovician) of the Canning Basin, Western Australia

Fig. 9. Protopanderodus gradatus Serpagli, 1974. All from sample WCB705/243. A–C, Sa element; (A), CPC39869, lateral view (IY128-010); (B), CPC39870, lateral view (IY128-011); (C), CPC39871, basal view (IY128-008). D–G, Sc element; (D), CPC39872, inner lateral view (IY128-001); (E), CPC39873, inner lateral view (IY128-002); (F), CPC39874, basal-inner lateral view (IY128-005); (G), CPC39875, upper-inner lateral view (IY128-009). H,I, P element, CPC39876, (H), inner lateral view (IY128-019); (I), basal-outer lateral view (IY128-020). J–P,?M element; J–K, CPC39877, (J), inner lateral view (IY129-037); (K), close-up inner lateral view, showing striae near the furrow and chevron-shaped pattern of striae adjacent to anterior margin (IY129-038); L,M, CPC39878, (L), outer lateral view (IY129-041); (M), anterior view (IY129-039); N–P, CPC39879, (N), inner lateral view (IY129-042); (O), close-up inner lateral view, showing striae near the furrow and chevron-shaped pattern of striae adjacent to anterior margin (IY129-043); (P), close up showing rounded boring hole on the surface (IY129-044). Scale bars 100 µm unless otherwise indicated.

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Jumping the green wall: the use of PNA-DNA clamps to enhance microbiome sampling depth in wildlife microbiome research

As microbiome research moves away from model organisms to wildlife, new challenges for microbiome high throughput sequencing arise caused by the variety of wildlife diets. High levels of contamination are commonly observed emanating from the host (mitochondria) or diet (chloroplast). Such high contamination levels affect the overall sequencing depth of wildlife samples thus decreasing statistical power and leading to poor performance in downstream analysis. We developed an amplification protocol utilizing PNA-DNA clamps to maximize the use of resources and to increase the sampling depth of true microbiome sequences in samples with high levels of plastid contamination. We chose two study organisms, a bat (Leptonyteris yerbabuenae) and a bird (Mimus parvulus), both relying on heavy plant-based diets that sometimes lead to traces of plant-based faecal material producing high contamination signals from chloroplasts and mitochondria. On average, our protocol yielded a 13-fold increase in bacterial sequence amplification compared with the standard protocol (Earth Microbiome Protocol) used in wildlife research. For both focal species, we were able significantly to increase the percentage of sequences available for downstream analyses after the filtering of plastids and mitochondria. Our study presents the first results obtained by using PNA-DNA clamps to block the PCR amplification of chloroplast and mitochondrial DNA from the diet in the gut microbiome of wildlife. The method involves a cost-effective molecular technique instead of the filtering out of unwanted sequencing reads. As 33% and 26% of birds and bats, respectively, have a plant-based diet, the tool that we present here will optimize the sequencing and analysis of wild microbiomes.

opencc-zeroAug 2021View details →

opencc-by-4.0Aug 2021View details →

Experimental verification of isotropic auxetic behaviour of hierarchical samples

Video of experimental test on a hierarchical auxetic and isotropic <a href="https://www.sciencedirect.com/topics/engineering/porous-medium">p</a>orous sample with extremely negative Poisson’s ratio, related to the publication: M. Morvaridi, G. Carta, F. Bosia, A. S. Gliozzi, N. M. Pugno, D. Misseroni, M. Brun, "Hierarchical auxetic and isotropic porous medium with extremely negative Poisson’s ratio", Extreme Mechanics Letters 48, 101405 (2021), https://doi.org/10.1016/j.eml.2021.101405.

opencc-by-4.0Dec 2020View details →

Seroprevalence of IgG antibodies against SARS coronavirus 2 in Belgium – a serial prospective cross-sectional nationwide study of residual samples (March – October 2020)

This dataset contains information on seven prospective cross-sectional nationwide residual sera collection rounds. The samples were analyzed for IgG antibodies against S1 proteins of SARS-CoV-2 with a semi-quantitative commercial ELISA (EuroImmun, Luebeck, Germany). We provide a CSV file containing the following variables: <ul> <li>code: unique sample code</li> <li>age_cat: age categories by 10-year age bands (0-10, 10-20, ..., 80-90, 90-Inf), the lower limit is included, e.g. 0-10 = [0,10)</li> <li>sex: sex (f = female, m = male)</li> <li>province: province of residence (11 categories)</li> <li>region: region of residence (3 categories: Brussels, Flanders, Walloon)</li> <li>collection_round: collection round (values 1 to 7)</li> <li>collection_start: start date of the collection round</li> <li>collection_end: end date of the collection round</li> <li>igg_orig: measured IgG OD value as character (note, a semi-quantitative ELISA was used, i.e. this should not be interpreted continiously)</li> <li>igg_cat: categorized IgG OD values <ul> <li>LoD: IgG OD < 0.15</li> <li>negative: 0.15 ≤ IgG OD < 0.8</li> <li>borderline: 0.8 ≤ IgG OD < 1.1</li> <li>positive: 1.1  ≤ IgG OD</li> </ul> </li> </ul> Please see publication mentioned underneath for more details (<a href="https://doi.org/10.1101/2020.06.08.20125179">https://doi.org/10.1101/2020.06.08.20125179</a>). Funding: This work received funding from the European Union's Horizon 2020 research and innovation program - project EpiPose (No 101003688), the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement 682540 TransMID), the Flemish Research Fund (FWO 1150017N) and from The Antwerp University Fund; which is a community of donors who contribute to research and education with their personal commitment through a donation, gift, bequest or through academic chairs. The funders had no role in study design, data collection, data analysis, data interpretation, writing or submitting of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

opencc-by-4.0Dec 2018View details →

Fig 1 in Is repeated cypermethrin fumigation dangerous for the mitochondrial DNA in dry insect samples?

Fig 1. Chemical structure of cypermethrin (C22H19Cl2NO3) which is the active insecticidal substance (6.25%) in commercial available smoke shells "Cytrol Super SG" (PelGar s.r.o./Agrochema družstvo Studenec, Czech Republic).

opencc-by-4.0Dec 2018View details →

Fig 3 in Is repeated cypermethrin fumigation dangerous for the mitochondrial DNA in dry insect samples?

Fig 3. Quality of PCR products of all 128 samples used in this study (barcoding region of the cytochrome oxidase I) checked on the 1% agarose gel in the horizontal electrophoresis. A1–D1 – run I. A1 – Oxythyrea funesta (fumigated samples), B1 – O. funesta (negative control), C1 – Supella longipalpa (fumigated samples), D1 – S. longipalpa (negative control). A2–D2 – run II (same legend as in run I). L = DNA ladder, N = negative control for PCR, P = positive control for PCR. Numbers of samples are abbreviation of musem IDs (NMPC-GAS-00XX, XX = numbers used in this figure). Red number = weak or not working sample.

Fig. 5 in Sampling mammalian carnivores in western Thailand: Issues of rarity and detectability

Fig. 5. Model averaged estimates of detection probability for 15 carnivore species by camera trapping plotted as a function of log10(weight in kg). Error bars represent 95% confidence intervals. Fitted line is derived from a second-order polynomial which described the best fit of the data. R2 is the coefficient of determination of the fitted line. The six species with lg(weight) greater than 1.0 are Asiatic golden cat, Asiatic jackal, dhole, sun bear, Asiatic black bear, and tiger, from left to right.

Fig. 4 in Sampling mammalian carnivores in western Thailand: Issues of rarity and detectability

Fig. 4. Model averaged estimates of detection probability across the models shown in Tables 2B and 2C for species functional groups (TRAIT; large vs small and terrestrial vs semi-arboreal behavior). Estimated detection probabilities of each species are also shown for TERRESTRIAL and SEMI-ARBOREAL species. Error bars represent 95% confidence intervals.

Fig. 3 in Sampling mammalian carnivores in western Thailand: Issues of rarity and detectability

Fig. 3. Model averaged estimates of detection probability of four functional groups of carnivores (top row) and for each of 15 species separately. Detection probabilities are estimated as functions of camera placement categories (road [no. cameras = 1], streams [5], trails [14], & trails by streams [tbs; 33]). Error bars represent 95% confidence intervals. Estimates were presented without error bars when standard errors could not be estimated. Estimates with 95% confidence intervals less than zero and/or greater than one indicate a lack of model convergence. n refers to the number of independent photographs used to estimate detection probabilities.

Fig. 2 in Sampling mammalian carnivores in western Thailand: Issues of rarity and detectability

Fig. 2. Species discovery curves generated from rarefaction for mammalian carnivores based on camera trap surveys at four study sites within mosaic forest types of Thung Yai between November 2007 and August 2008. Bars represent 95% confidence intervals of the estimates scaled by survey effort (camera trap nights) combined across all active camera locations (n = 53). Curves were estimated using package BiodiversityR in program R.

Fig. 1 in Sampling mammalian carnivores in western Thailand: Issues of rarity and detectability

Fig. 1. Map of Thung Yai Naresuan Wildlife Sanctuary showing camera trap polygons in four study sites, from south to north, Headquarters (HQ), Tikong (TK), Sesawo (SSW), and Mae Gatha (MGT) between November 2007 and August 2008.

opencc-by-4.0Aug 2003View details →

Fig. 24 in Phylogenetic Studies On Didelphid Marsupials Ii. Nonmolecular Data And New Irbp Sequences: Separate And Combined Analyses Of Didelphine Relationships With Denser Taxon Sampling

Fig. 24. Tlacuatzin canescens, photographed by Gerardo Ceballos in March 1995 at the ChamelaCuixmala Biosphere Reserve, Jalisco, Mexico. Specimens from southern populations (especially topotypical material from Oaxaca) are markedly grayer than this individual.

opencc-by-4.0Aug 2003View details →

Fig. 12 in Phylogenetic Studies On Didelphid Marsupials Ii. Nonmolecular Data And New Irbp Sequences: Separate And Combined Analyses Of Didelphine Relationships With Denser Taxon Sampling

Fig. 12. Bivariate comparison of two dental proportions discussed in the text, with illustrated examples of contrasting morphologies. Closed curves delimit sets of taxa assigned to alternative states of character 57. Taxon labels: 1, Caluromys lanatus; 2, Caluromys philander; 3, Caluromysiops irrupta; 4, Chironectes minimus; 5, Didelphis albiventris; 6, Didelphis marsupialis; 7, Didelphis virginiana; 8, Glironia venusta; 9, Gracilinanus microtarsus; 10, Lestodelphys halli; 11, Lutreolina crassicaudata; 12, Marmosa canescens; 13, Marmosa lepida; 14, Marmosa mexicana; 15, Marmosa murina; 16, Marmosa robinsoni; 17, Marmosa rubra; 18, Marmosops impavidus; 19, Marmosops incanus; 20, Marmosops noctivagus; 21, Marmosops parvidens; 22, Marmosops pinheiroi; 23, Metachirus nudicaudatus; 24, Micoureus demerarae; 25, Micoureus paraguayanus; 26, Micoureus regina; 27, Monodelphis adusta; 28, Monodelphis brevicaudata; 29, Monodelphis emiliae; 30, Monodelphis theresa; 31, Philander frenata; 32, Philander mcilhennyi; 33, Philander opossum; 34, Thylamys pallidior; 35, Thylamys venustus. Other labels: MC, metacrista; PC, postprotocrista.