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Fig 2 in Four principles to establish a universal virus taxonomy
Fig 2. Structure-based dendrogram of capsid proteins of members of the kingdom Bamfordvirae. Structure-based phylogenetic tree inferred from major capsid protein (MCP) structures of the members of the kingdom Bamfordvirae in the Varidnaviria realm. Members of Bamfordvirae encode a vertical double-jelly roll fold MCP, which is the hallmark protein of this group of viruses. Next to each MCP structure are the virus name (top), the phylum (middle), and family (bottom), with "Faustovirus" not yet officially classified and Finnlakeviridae not yet assigned to any higher taxon. The evolutionary distances across the depicted members of the originally called PRD1-adenovirus viral lineage [67] were calculated with the Homologous Structure Finder software [50] and depicted with PHYLIP (https://evolution.genetics.washington.edu/phylip.html); the evolutionary distances are shown next to each branch. The protein data bank identifiers (PDBid) for the structures are as follows: PRD1: PDBid 1HX6; PBCV-1: 1M3Y; adenovirus: 1P2Z; STIV: 2BBD; Vaccinia D13: 2YGB; Sputnik: 3J26; Faustovirus: 5J7O; FLiP: 5OAC; ASFV p72: 6KU9; PM2: 2W0C. Adapted from [62]. https://doi.org/10.1371/journal.pbio.3001922.g002
Fig 1 in Four principles to establish a universal virus taxonomy
Fig 1. Ranks used in virus taxonomy. Schematic depiction of the 15-rank taxonomic framework used by the ICTV. It includes the methodologies that may be used to determine virus evolutionary relationships and make assignments at each rank. The pyramid shape indicates that the number of taxa increases from the top rank (realm) to the most basal rank (species, Sp.). The names of the 15 ranks are shown on the left of the pyramid, and the methodologies are on the right (AAS, amino acid sequence similarity; NS, nucleotide sequence similarity). The pyramid includes a hypothetical example of the taxonomy of a realm, indicating the number of taxa at each rank (filled circles). The phenotypic properties of classified viruses that may inform rank placements are depicted below the pyramid. https://doi.org/10.1371/journal.pbio.3001922.g001
[Dataset] Data for the course "Population Genomics" at Aarhus University
<p>Datasets, conda environments and Softwares for the course "Population Genomics" of Prof Kasper Munch. This course material is maintained by the <a href="https://hds-sandbox.github.io/">health data science sandbox</a>. This <a href="https://hds-sandbox.github.io/PopulationGenomicsCourse/">webpage</a> shows the latest version of the course material.</p> <ol> <li> Data.tar.gz Contains the datasets and executable files for some of the softwares<br> You can unpack by simply doing<br> tar -zxf Data.tar.gz -C ./<br> This will create a folder called Data with the uncompressed material inside</li> <li>Course_Env.packed.tar.gz Contains the conda environment used for the course. This needs to be unpacked to adjust all the prefixes (Note this environment is created on Ubuntu 22.10). You do this in the command line by <ol> <li>creating the folder Course_Env: mkdir Course_Env</li> <li>untar the file: tar -zxf Course_Env.packed.tar.gz -C Course_Env</li> <li>Activate the environment: conda activate ./Course_Env</li> <li>Run the unpacking script (it can take quite some time to get it done): conda-unpack</li> </ol> </li> <li>Course_Env.unpacked.tar.gz The same environment as above, but will work only if untarred into the folder /usr/Material - so use the version above if you are using it in another folder. This file is mostly to execute the course in our own cloud environment.</li> <li>environment_with_args.yml The file needed to generate the conda environment. Create and activate the environment with the following commands: <ol> <li>conda env create -f environment_with_args.yml -p ./Course_Env</li> <li>conda activate ./Course_Env</li> </ol> </li> </ol> <p> </p> <p>The data is connected to the following repository:<a href="https:// github.com/hds-sandbox/Popgen_course_aarhus."> https://github.com/hds-sandbox/Popgen_course_aarhus.</a> The original course material from Prof Kasper Munch is at <a href="https://github.com/kaspermunch/PopulationGenomicsCourse">https://github.com/kaspermunch/PopulationGenomicsCourse</a>.</p> <p> </p> <p><strong>Description</strong></p> <p>The participants will after the course have detailed knowledge of the methods and applications required to perform a typical population genomic study.</p> <p>The participants must at the end of the course be able to:</p> <ul> <li>Identify an experimental platform relevant to a population genomic analysis.</li> <li>Apply commonly used population genomic methods.</li> <li>Explain the theory behind common population genomic methods.</li> <li>Reflect on strengths and limitations of population genomic methods.</li> <li>Interpret and analyze results of population genomic inference.</li> <li>Formulate population genetics hypotheses based on data</li> </ul> <p>The course introduces key concepts in population genomics from generation of population genetic data sets to the most common population genetic analyses and association studies. The first part of the course focuses on generation of population genetic data sets. The second part introduces the most common population genetic analyses and their theoretical background. Here topics include analysis of demography, population structure, recombination and selection. The last part of the course focus on applications of population genetic data sets for association studies in relation to human health.</p> <p>Curriculum</p> <p>The curriculum for each week is listed below. "Coop" refers to a set of <a href="https://github.com/cooplab/popgen-notes/releases/download/v1.2/minicoop.pdf">lecture notes by Graham Coop</a> that we will use throughout the course.</p> <p>Course plan</p> <ol> <li>Course intro and overview: <ul> <li>Coop chapters 1, 2, 3, <a href="https://www.nature.com/articles/nature18964">Paper: Genome Diversity Project</a></li> </ul> </li> <li>Drift and the coalescent: <ul> <li>Coop chapter 4; <a href="https://www.nature.com/articles/ng.3036">Paper: Platypus</a></li> <li>Exercise: Read mapping and base calling</li> </ul> </li> <li>Recombination: <ul> <li>Lecture: <a href="https://royalsocietypublishing.org/doi/10.1098/rstb.2016.0455">Review: Recombination in eukaryotes</a>, <a href="https://www.nature.com/articles/s41576-020-0240-1">Review: Recombination rate estimation</a></li> <li>Exercise: Phasing and recombination rate</li> </ul> </li> <li>Population strucure and incomplete lineage sorting: <ul> <li>Lecture: Coop chapter 6, <a href="https://doi.org/10.1146/annurev-genet-120213-092532">Review: Incomplete lineage sorting</a></li> <li>Exercise: Working with VCF files</li> </ul> </li> <li>Hidden Markov models: <ul> <li>Lecture: Durbin chapter 3, <a href="https://www.nature.com/articles/nature07331">Paper: population structure</a></li> <li>Exercise: Inference of population structure and admixture</li> </ul> </li> <li>Ancestral recombination graphs: <ul> <li>Lecture: <a href="https://bmcgenomdata.biomedcentral.com/articles/10.1186/1471-2156-7-16">Paper: Approximating the ARG</a>, <a href="https://www.nature.com/articles/s41588-019-0484-x">Paper: Tree inference</a></li> <li>Exercise: ARG dashboard exercises + Inference of trees along sequence</li> </ul> </li> <li>Past population demography: <ul> <li>Lecture: Coop chapter 4, <a href="https://www.nature.com/articles/nature10231">Paper: PSMC</a>, revisit <a href="https://www.nature.com/articles/s41588-019-0484-x">Paper: Tree inference</a></li> <li>Exercise: Inferring historical populations</li> </ul> </li> <li>Direct and linked selection: <ul> <li>Lecture: Coop chapters 12, 13, revisit <a href="https://www.nature.com/articles/s41588-019-0484-x">Paper: Tree inference</a></li> </ul> </li> <li>Admixture: <ul> <li>Lecture: <a href="https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007349">Review: Admixture</a>, <a href="https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007641">Paper: Admixture inference</a></li> <li>Exercise: Detecting archaic ancestry in modern humans</li> </ul> </li> <li>Genome-wide association study (GWAS): <ul> <li>Lecture: <a href="https://github.com/cooplab/popgen-notes/releases/download/v1.2/release_popgen_notes.pdf">Coop lecture notes</a> 99-120</li> <li>Exercise: GWAS quality control</li> </ul> </li> <li>Heritability: <ul> <li>Lecture: Coop Lecture notes Sec. 2.2 (p23-36) + Chap. 7 (p119-142)</li> <li>Exercise: Association testing</li> </ul> </li> <li>Evolution and disease: <ul> <li>Lecture: Coop Lecture notes Sec. 11.0.1 (p217-221)</li> <li>Exercise: Estimating heritability</li> </ul> </li> </ol>
Longitudinal Data on the Specific and Universal Interventions Received / Datos Longitudinales sobre las Intervenciones Específicas y Universales Recibidas // SUJ-2_Data_CC-IC_T1toT6
<p>This is a longitudinal file that provides information on the interventions received by the participant in each grade, from first to fourth grade of primary education. Interventions can be universal or specific. Both types of interventions are organized according to the focus and/or area of intervention (teacher, child, peers, parents). The file reports the types of interventions that the participant has received in each grade in each focus and their breakdown into concrete interventions. It also reports in which courses the participant meets the criteria to be a candidate for specific interventions and the variables used to identify the candidates (variables of concern to the teacher and sociometric status variables). The information was provided by the researchers. Data were collected in public schools in Castellón (Spain) mostly in the spring of each school year, but many variables were also measured in the fall, from the first grade of primary education to the sixth grade, from the 2009-10 school year to the 2015-16 school year. To learn more about our research and access to other datasets of this or other measures, follow the link <a href="https://www.uji.es/departaments/psi/base/opengrei/">GREI Longitudinal Project</a></p> <p> </p> <p>Se trata de un fichero longitudinal que aporta información sobre las intervenciones que ha recibido el participante en cada curso, de primero a cuarto. Las intervenciones pueden ser universales o específicas. Ambos tipos de intervenciones se organizan en función del foco y/o área de intervención (profesor, niño-a, iguales, padres). En el fichero se informa de los tipos de intervenciones que el participante ha recibido en cada foco durante cada curso y de su desglose en intervenciones concretas. También se informa de en qué cursos el participante cumple criterios para ser candidato a recibir intervenciones específicas y de las variables que se han utilizado para identificar a los candidatos (variables preocupa al profesor y variables de estatus sociométrico). La información ha sido proporcionada por los investigadores. Los datos fueron recogidos en centros públicos de Castellón (España) mayoritariamente en la primavera de cada curso, pero muchas variables también fueron medidas en otoño, desde el primer curso de educación primaria a sexto, desde el curso escolar 2009-10 al curso escolar 2015-16. Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace <a href="https://www.uji.es/departaments/psi/base/opengrei/">Proyecto Longitudinal GREI</a></p>
Bibliographic data on datasets affiliated to Poznan University of Technology and indexed in Data Citation Index (retrieved by Web of Science service in January 2023))
<p>The file contains the number of datasets published by the researchers affiliated to Poznan University of Technology and indexed in Data Citation Index provided by Web of Science (database updated 10.01.2023). The Search was performed using the name of institution in the 'Affiliation' field. Dataset contains two files in two diffrent formats: plain text and xls.</p>
Health-related quality of life and musculoskeletal pain in health sciences university students
<p><strong>Purpose: </strong>To analyse the health-related quality of life and associated variables in health science university students. Secondly, to determine the prevalence of musculoskeletal pain and central sensitisation, and the differences by gender.</p> <p><strong>Methods: </strong>Cross-sectional study among health sciences university students, using anonymous, self-administered questionnaires (SF-12, Nordic Musculoskeletal Questionnaire (NMQ), Numeric Pain Rating Scale (NRS), Central Sensitisation Inventory (SCI)).</p> <p><strong>Results: </strong>Three hundred and thirty eight students were included (76.3% female; age 22.5±4.9 years). The mean SF-12 Physical Component Summary was higher than the mean of 50 in the Spanish adult population as a whole for both female and male students (54.6 vs. 55.9; p=0.02), whereas the mean SF-12 Mental Component Summary was lower than the population mean (36.7 vs. 42.8; p<0.001). These differences persisted when compared with age- and sex-matched population standardized scores.</p> <p>A high prevalence of musculoskeletal pain was observed, particularly in the back area. Pain was generalised, chronic, of moderate intensity, and more frequent in women.</p> <p>Variables associated with worse physical health were being female (B=-1.5; p=0.035), having a disability (B=-4.6; p<0.001), chronic musculoskeletal pain at any site (B=-2.1; p=0.001), and a score ≥40 on the SCI (B=-3.2: p<0.001). The only variable associated with poorer mental health was the SCI score.</p> <p><strong>Conclusion: </strong>Health sciences university students report a significant deterioration in mental health and a high prevalence of musculoskeletal pain, with symptoms of central sensitisation associated with poorer physical and mental health. Women have worse mental health and a higher prevalence, intensity and chronification of musculoskeletal pain.</p>
Is plagiarism on the rise in Ethiopian politics? The case of three Master's theses at Addis Ababa University
<p>After an inquiry for plagiarism, the University of Düsseldorf in Germany <a href="https://www.theguardian.com/world/2013/feb/09/german-education-minister-quits-phd-plagiarism">revoked Annette Schavan's doctorate degree in 2013</a>. Schavan (CDU) served as Germany's Federal Minister of Education from 2005 to 2013, and resigned after her PhD was revoked. Karl-Theodor zu Guttenberg (CDU), Germany's defense minister, <a href="https://www.theguardian.com/world/2011/mar/01/german-defence-minister-resigns-plagiarism">resigned in 2011</a> when plagiarism was discovered in his PhD dissertation. As a result, the VroniPlag Wiki was established, where the level of plagiarism in German doctorate theses is investigated and documented via crowdsourcing. As a consequence, dozens of politicians were deprived of their degrees, voluntarily abandoned them, or fled politics. <a href="https://www.faz.net/aktuell/karriere-hochschule/hoersaal/franziska-giffey-verzicht-auf-doktortitel-ist-nicht-moeglich-18746134.html">Franziska Giffey (SPD) and Martin Huber (CSU)</a> are the most recent additions in 2023.</p> <p>The prestige of having an advanced degree is quite high in Ethiopia, as it is in Germany, and as a result, the temptation for shortcuts may be quite strong. We documented three instances of possible MSc thesis plagiarism at Addis Abeba University (AAU): Abraham Belay (2007), Takele Uma (2014), and Dagmawit Moges (2019), all of whom are (or were until recently) ministers in the Ethiopian government.</p> <p>The MSc theses were formally analysed for similarity with earlier works using <a href="https://www.turnitin.com/">Turnitin anti-plagiarism software</a>. The automated output was then further analysed. Similarities with works produced after or concurrently with the publishing date of the MSc theses were omitted from the similarity report; this pertains to a few instances of theses and articles in predatory journals that plagiarized passages from those theses. Also short similarities (eight words or less) were removed from the report. After that, the similarity scores remain high for the three MSc theses, with numerous fully copied paragraphs and sections.</p> <p><strong>Abraham Belay</strong> published an MSc thesis on “<a href="http://etd.aau.edu.et/bitstream/handle/123456789/6598/Abraham%20belay.pdf?sequence=1&isAllowed=y">DSP based vector control of induction motor</a>” at AAU’s Department of Electrical and Computer Engineering in 2007 (<a href="https://web.archive.org/web/20230124144908/http:/etd.aau.edu.et/bitstream/handle/123456789/6598/Abraham%20belay.pdf">archive</a>). In 2020, Abraham Belay (Prosperity Party) became minister of Innovation and Technology and in 2021 Minister of Defence. His MSc thesis presents 62% similarities with earlier work by other authors. See <strong>Annex A</strong>.</p> <p><strong>Takele Uma</strong> published an MSc thesis on “<a href="http://etd.aau.edu.et/handle/123456789/11897">Environmental and Economic Benefits of Bioslurry from Coffee Husk Relative to Chemical Fertilizer</a>” at AAU’s School of Chemical and Bio-Engineering in 2014 (<a href="https://web.archive.org/web/20230409120611/http:/etd.aau.edu.et/bitstream/handle/123456789/11897/Takele%20Uma.pdf">archive</a>). Takele Uma (PP) was mayor of Addis Ababa from 2018 to 2020 and minister of Mines and Petroleum of Ethiopia from 2020 to 2023. His MSc thesis presents 38% similarities with earlier work. See <strong>Annex B</strong>.</p> <p><strong>Dagmawit Moges</strong> (PP) published an MSc thesis on “<a href="http://etd.aau.edu.et/handle/123456789/19432">The Challenges and Prospects of Dynamic Electronic Service in Strengthening Demand-Responsive Transportation System in Addis Ababa, Ethiopia</a>” at AAU’s Department of Public Administration and Development Management in 2019 (<a href="https://web.archive.org/web/20230409120757/http:/etd.aau.edu.et/bitstream/handle/123456789/19432/Dagmawit%20Moges.pdf">archive</a>). Dagmawit was minister of Transport and Communications of Ethiopia from 2018 to 2023. In March 2023, she became director at the Peace Fund Secretariat of the African Union Commission. She <a href="https://smartermobility-africa.com/speakers/h-e-dagmawit-moges-bekele/">chairs also the boards</a> of Woldiya University, the Ethiopian Post, and the Ethiopian Roads Authority. Her MSc thesis presents 33% similarities with earlier work. <strong>See Annex C</strong>.</p> <p>We encourage readers to review the entire Turnitin reports for the three theses. It is not impossible that further paragraphs extracted from grey literature were missed by the Turnitin algorithms.</p> <p>On 28 February 2020, Addis Ababa University sent a <a href="https://archive.today/2023.04.08-084235/https:/t.me/Addisababauniversity/105">message in Amharic on its Telegram</a> channel, stating in substance that AAU has an anti-plagiarism policy, since plagiarism has been a significant hindrance to the University's efforts to provide excellent education. AAU further states that, earlier on, it has revoked a master's degree it had issued to a student due to plagiarism. It is only logical, then, that the three MSc theses annexed be formally scrutinized for plagiarism and the degrees possibly rescinded.</p> <p>Last but not least, examining the legitimacy of the country's politicians' academic degrees would necessitate a collaborative effort, and we urge concerned academics, students, and alumni of Ethiopian universities to initiate a crowdsourcing effort to detect plagiarism in theses, as is done in Germany with <a href="https://en.wikipedia.org/wiki/VroniPlag_Wiki">VroniPlag Wiki</a> and in Russia with <a href="https://en.wikipedia.org/wiki/Dissernet">Dissernet</a>.</p> <p> </p> <p><strong>Acknowledgments</strong></p> <p>The authors acknowledge the owners of Twitter accounts @EgleGeek and @Zeate1 for pointing out that the three MSc theses addressed here reused texts from previous works. We would also like to thank Alex de Waal (Tufts University, Medford, MA, USA) and Boud Roukema (Nicolaus Copernicus University, Toruń, Poland) for exchanges of thoughts.</p> <p> </p> <p><strong>Annexes</strong></p> <p>Annex A. <a href="https://zenodo.org/record/7810624/files/Annex%20A%20-%20Abraham%20belay%20MSc%20thesis%20SimCheck2.pdf">Turnitin similarity report of the MSc thesis by Abraham Belay (2007)</a>.</p> <p>Annex B. <a href="https://zenodo.org/record/7810624/files/Annex%20B%20-%20Takele%20Uma%20MSc%20thesis%20similarity%20check.pdf">Turnitin similarity report of the MSc thesis by Takele Uma (2014).</a></p> <p>Annex C. <a href="https://zenodo.org/record/7810624/files/Annex%20C%20-%20Dagmawit%20Moges%20MSc%20thesis%20similarity%20check.pdf">Turnitin similarity report of the MSc thesis by Dagmawit Moges (2019)</a>.</p>
The tertiary education system of Congolese universities between laxity and purism.
<p>Realizing the crisis of the tertiary education system of the Congolese universities </p> <p>characterizing her educational partners on even to a culture laxity a suicidal lethargy, this </p> <p>article that one opts for the implementation of a purism mechanism to grandma to reveal the </p> <p>challenge. This prospective reflection wants to open to the creative conscience of solutions </p> <p>salvage therefore through the internationalization that became a condition one is not able to </p> <p>more fundamental for the effectively of the role of the academic teaching and an implicit part </p> <p>of the applicable and qualitatively superior research in terms of teaching, formation, research </p> <p>and services given back to the community, undertaken by the Congolese universities in view to </p> <p>augur the competitive degree of the world education system. </p>
De La Salle University – Outdoor Mirrors and Reflective Surfaces (DLSU-OMRS) Dataset
<p><strong>The De La Salle University – Outdoor Mirrors and Reflective Surfaces (DLSU-OMRS) dataset contains 454 images of outdoor mirrors and reflective surfaces, along with their corresponding ground-truth masks for segmentation</strong>. The images were scraped from Shutterstock using the key phrases <em>outdoor mirror</em> and <em>street mirror</em> and manually filtered to remove duplicates and heavily manipulated photos. Ground-truth masks were produced through manual segmentation.</p> <p>The images have their respective licenses, and the ground-truth masks are licensed under the BSD 3-Clause "New" or "Revised" License. The use of this dataset is restricted to noncommercial purposes only.</p> <p>More details can be found in the paper "<strong>Designing a Lightweight Edge-Guided Convolutional Neural Network for Segmenting Mirrors and Reflective Surfaces</strong>," which was accepted for full paper presentation at the <strong>2023 International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG 2023)</strong>. The project page is <a href="https://github.com/memgonzales/mirror-segmentation">https://github.com/memgonzales/mirror-segmentation</a>. The paper is published in <em>Computer Science Research Notes</em>: <a href="http://wscg.zcu.cz/WSCG2023/full/E59-full.pdf">http://wscg.zcu.cz/WSCG2023/full/E59-full.pdf</a>.</p>
O2-O2, SO2, BrO, and IO differential slant column densities (dSCDs) measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Maido Observatory during April 29, 2018 and May 4, 2018
<p>Description: O<sub>2</sub>-O<sub>2</sub>, SO<sub>2</sub>, BrO, and IO differential slant column densities (dSCDs) measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Maido Observatory during April 29, 2018 and May 4, 2018.</p> <p>Instrument: University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS)<br> Instrument reference: Coburn et al. (2011); doi:10.5194/amt-4-2421-2011<br> Instrument contact: Christopher F. Lee (christopher.f.lee@colorado.edu)<br> Instrument PI: Rainer Volkamer (rainer.volkamer@colorado.edu)<br> <br> Measurement site: Maido Observatory, Reunion Island<br> Longitude: 55.384 degrees East<br> Latitude: 21.080 degrees South<br> Altitude: 2160 meters above sea level<br> Azimuth angle: Approximately 100 degrees clockwise from north<br> <br> The detection limit is defined as (2*Measured RMS) / (Maximum differential absorption cross section), where RMS = root-mean-square noise of spectral signal not accounted for by DOAS fit parameters [optical density units]. The maximum differential absorption cross sections used are 7.0e-21 [cm<sup>2</sup>] for SO<sub>2</sub>, 2.6e-17 [cm<sup>2</sup>] for BrO, and 3.5e-17 [cm<sup>2</sup>] for IO. Detection limits for SO<sub>2</sub> dSCDs, BrO dSCDs, and IO dSCDs are only reported during periods of significant SO<sub>2</sub> detection. BrO to SO<sub>2</sub> ratios are only reported during periods when both BrO dSCDs and SO<sub>2</sub> dSCDs are above the detection limit.</p> <p>Local time (RET) is UTC+4.<br> <br> Column 1: UTC start datetime (yyyy-mm-dd HH:MM:SS)<br> Column 2: UTC center datetime (yyyy-mm-dd HH:MM:SS)<br> Column 3: UTC stop datetime (yyyy-mm-dd HH:MM:SS)<br> Column 4: Elevation angle above the horizon (degrees)<br> Column 5: O<sub>2</sub>-O<sub>2</sub> dSCD [molec<sup>2</sup> cm<sup>-5</sup>]<br> Column 6: Spectral fit error for O<sub>2</sub>-O<sub>2</sub> dSCD [molec<sup>-2</sup> cm<sup>-5</sup>]<br> Column 7: SO<sub>2</sub> dSCD [molec cm<sup>-2</sup>]<br> Column 8: Spectral fit error for SO<sub>2</sub> dSCD [molec cm<sup>-2</sup>]<br> Column 9: Detection limit for SO<sub>2</sub> dSCD [molec cm<sup>-2</sup>]<br> Column 10: BrO dSCD [molec cm<sup>-2</sup>]<br> Column 11: Spectral fit error for BrO dSCD [molec cm<sup>-2</sup>]<br> Column 12: Detection limit for BrO dSCD [molec cm<sup>-2</sup>]<br> Column 13: IO dSCD [molec cm<sup>-2</sup>]<br> Column 14: Spectral fit error for IO dSCD [molec cm<sup>-2</sup>]<br> Column 15: Detection limit for IO dSCD [molec cm<sup>-2</sup>]<br> Column 16: Ratio of BrO dSCDs to SO<sub>2</sub> dSCDs<br> Column 17: Error in ratio of BrO dSCDs to SO<sub>2</sub> dSCDs</p>
Data from "Higher mortality is not a universal cost of dispersal: A case study in African wild dogs"
<p>Mortality is considered one of the main costs of dispersal. A reliable evaluation of mortality, however, is often hindered by a lack of information about the fate of individuals that disappear under unexplained circumstances (i.e. missing individuals). Here, we addressed this uncertainty by applying a Bayesian mortality analysis that inferred the fate of missing individuals based on information from individuals with known fate. Specifically, we tested the hypothesis that mortality during dispersal is higher than mortality among non-disperser using 32 years of mark-resighting data from a free-ranging population of the endangered African wild dog (<em>Lycaon pictus</em>) in northern Botswana. Contrary to expectations, we found that mortality during dispersal was lower than mortality among non-dispersers, indicating that higher mortality is not a universal cost of dispersal. Our findings suggest group living can incur costs for certain age classes, such as limited access to resources as group density increases, that exceed the mortality costs associated with dispersal. By challenging the accepted expectation of higher mortality during dispersal, we urge for further investigations of this key life-history trait, and propose a robust statistical approach to reduce bias in mortality estimates.</p>
Supplementary code and data for the paper: 'The fall of genres that did not happen: formalising history of the "universal" semantics of Russian iambic tetrameter'
<p>The dataset provides preprocessed data and the full code used in the paper 'The fall of genres that did not happen: formalising history of the "universal" semantics of Russian iambic tetrameter'. The code can be also be accessed as rendered notebooks on Github.</p><p>The dataset is structured as follows:</p><ul><li><i>data/ </i>: This folder contains preprocessed data,including a sampled corpus of periodicals and a document-term matrix used for topic modelling; </li><li><i>scr/</i> : The code used for the analysis, with separate scripts for figures; </li><li><i>plots/</i> : The figures used in the paper, which correspond to the aforementioned code.</li></ul>
Hierarchical universal matrices for H(curl) tetrahedral finite elements
<p>The contents of this repository are associated with the paper:</p> <p>[1] L. L. Toth, A. Amor-Martin, and R. Dyczij-Edlinger, "Hierarchical Universal Matrices for Curvilinear Tetrahedral H(curl) Finite Elements," submitted to IEEE Transactions on Antennas and Propagation.</p> <p>The subdirectory UniversalMatrices contains mathematical formulas for hierarchical L2 and H(curl) basis functions, the corresponding universal matrices (UM), and a MATLAB script for generating these UMs. The bases and UMs are given in two different formats:</p> <p>*.mat MATLAB data file with a MATLAB structure,</p> <p>*.xml file with a structure.</p> <p>The subdirectory MATLAB_TestCode contains MATLAB scripts and input data for reproducing the numerical results given in [1].</p> <p>For details, see the README.txt files in the bottom-level directories.</p>
Datasets for Universal closed-tube barcoding for monitoring the shark and ray trade in megadiverse conservation hotspots
<p>Data repository accompanying manuscript titled of "Universal closed-tube barcoding for monitoring the shark and ray trade in megadiverse conservation hotspots" by Andhika Prima Prasetyo et.al</p>
Dataset of Bibliometric and Wikipedia Indicators of Universities
<p>This dataset is related to the paper: "From academic to media capital: To what extent does the scientific reputation of universities translate into Wikipedia attention?" It contains for each university different bibliometric and Wikipedia indicators.</p> <table align="center"> <thead> <tr> <th scope="col">General</th> <th scope="col">Bibliometrics</th> <th scope="col">Wikipedia</th> </tr> </thead> <tbody> <tr> <td>id</td> <td>impact_P</td> <td>langlinks</td> </tr> <tr> <td>grid_id</td> <td>impact_P_Bio_Health</td> <td>total_views</td> </tr> <tr> <td>short_name</td> <td>impact_P_Life_Earth</td> <td>local_views</td> </tr> <tr> <td>full_name</td> <td>impact_P_Math_Comp</td> <td>int_views</td> </tr> <tr> <td>Country</td> <td>impact_P_Phy_Eng</td> <td>rest_views</td> </tr> <tr> <td>established_year</td> <td>impact_P_Soc_Hum</td> <td>editors</td> </tr> <tr> <td>age</td> <td>P_top10</td> <td>revisions</td> </tr> <tr> <td>Continent</td> <td>collab_P</td> <td>words</td> </tr> <tr> <td> </td> <td>P_int_collab</td> <td>unique_references</td> </tr> <tr> <td> </td> <td>P_OA</td> <td>characters</td> </tr> <tr> <td> </td> <td>TCS</td> <td>sections</td> </tr> <tr> <td> </td> <td>TNCS</td> <td> </td> </tr> <tr> <td> </td> <td>P_industry_collab</td> <td> </td> </tr> <tr> <td> </td> <td>PA_F_MF</td> <td> </td> </tr> <tr> <td> </td> <td>dimensions_wos</td> <td> </td> </tr> <tr> <td> </td> <td>topic_specialisation</td> <td> </td> </tr> <tr> <td> </td> <td>topic_specialisation_bio</td> <td> </td> </tr> <tr> <td> </td> <td>topic_specialisation_eng</td> <td> </td> </tr> <tr> <td> </td> <td>gini_production</td> <td> </td> </tr> </tbody> </table> <p> </p>
Datasets for "Observation of universal Hall response in strongly interacting Fermions"
<p>This submission includes the datasets shown in the figures of journal article</p> <p>"Observation of universal Hall response in strongly interacting Fermions" by T.-W. Zhou et al., Science (2023).</p> <p>The naming of the files corresponds to the figure numbering in the published article:<br> FIG-x for figures in the main text<br> FIG-Sxx for figures in the Supplementary Materials </p>
Stereo PIV measurement of open channel flows in RA8 flume at the University of Sheffield
<p>Stereo PIV measurement of open channel flows in RA8 flume at the University of Sheffield</p> <p>Six flow conditions over a rough bed of spheres with 24mm diameter. PIV plane was at the centerline of the flow shining through the bed of spheres. Where the laser PIV plane shone up, the spheres were replaced with translucent hollow spheres to allow the light to go through. Gradient of the flow was 0.001.</p> <table> <tbody> <tr> <td>Water Depth</td> <td>Flow rate</td> <td>Velocity</td> <td>Reynolds’ number</td> <td>Manning’s number</td> <td>Froude number</td> <td>Weber number</td> <td>Relative Submergence</td> </tr> <tr> <td>(mm)</td> <td>(l/s)</td> <td>(m/s)</td> <td>(with depth)</td> </tr> <tr> <td>49</td> <td>1.87</td> <td>0.08</td> <td>3,740</td> <td>0.049</td> <td>0.11</td> <td>3.96</td> <td>2.04</td> </tr> <tr> <td>69</td> <td>5.05</td> <td>0.15</td> <td>10,100</td> <td>0.031</td> <td>0.18</td> <td>20.53</td> <td>2.88</td> </tr> <tr> <td>89</td> <td>7.46</td> <td>0.17</td> <td>14,920</td> <td>0.031</td> <td>0.18</td> <td>34.74</td> <td>3.71</td> </tr> <tr> <td>109</td> <td>11.21</td> <td>0.21</td> <td>22,420</td> <td>0.028</td> <td>0.2</td> <td>64.05</td> <td>4.54</td> </tr> <tr> <td>129</td> <td>15.4</td> <td>0.24</td> <td>30,800</td> <td>0.026</td> <td>0.21</td> <td>102.14</td> <td>5.38</td> </tr> <tr> <td>149</td> <td>20.7</td> <td>0.28</td> <td>41,400</td> <td>0.023</td> <td>0.23</td> <td>159.77</td> <td>6.21</td> </tr> </tbody> </table>
University Helpdesk Support Data Set
<p>This repository contains the data set presented in the publication "Identifying NFRs conflicts using quality ontologies" by Al Balushi et al. [1]. The data set has been extracted from the manuscript for easier reuse.</p> <p>[1] Al Balushi, T., Khod, O., Sampaio, P. R. F., Patel, M., Manchester, B. S. W., Corcho, O., & Loucopoulos, P. (2008). Identifying NFRs conflicts using quality ontologies. <em>SEKE 2008</em>, 929.</p>
Dataset of Universal 20 patterns with references
<p>A continuously growing dataset of more than 2000 languages, with references. A previous sample of 1687 languages is also available at the Terraling website <a href="http://terraling.com/groups/15">https://terraling.com/groups/15</a>.</p>
A Universe of Sound: Processing NASA Data into Sonifications to Explore Participant Response
<p>Files containing additional data for the first and second open-ended response questions of the survey discussed in Section 3.3 of the paper "A Universe of Sound: Processing NASA Data into Sonifications to Explore Participant Response" and text descriptions of the associated sonifications of three astronomical objects (the Galactic Center, Cassiopeia A, and the Chandra Deep Field South).</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.