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text-fig. 51. Strict consensus tree of several sets of 32766 trees. Tree length is 657 steps, CI 0-417, RI 0-746, and RCI 0-311. in The interrelationships and evolution of basal theropod dinosaurs
text-fig. 51. Strict consensus tree of several sets of 32766 trees. Tree length is 657 steps, CI 0-417, RI 0-746, and RCI 0-311.
text-fig. 54. Reduced consensus tree of the pruned data matrix after the deletion of Shuvosaurus, Segisaurus, and Poekilopleuron. Named nodes: 1, Saurischia; 4, Herrerasauridae; 5, Neotheropoda; 6, Coelophysoidea; 8, Coelophysidae; 9, Liliensternus', 12, Ceratosauria; 13, Abelisauroidea; 14, etanurae; 16, Camosauria; 17, Spinosauroidea; 20, Allosauroidea; 24, Coelurosauria; 26, Coeluridae; 27, Compsognathinae; 30, Tyrannosauroidea; 34, Maniraptora; 37, Deinonychosauria. in The interrelationships and evolution of basal theropod dinosaurs
text-fig. 54. Reduced consensus tree of the pruned data matrix after the deletion of Shuvosaurus, Segisaurus, and Poekilopleuron. Named nodes: 1, Saurischia; 4, Herrerasauridae; 5, Neotheropoda; 6, Coelophysoidea; 8, Coelophysidae; 9, Liliensternus', 12, Ceratosauria; 13, Abelisauroidea; 14, etanurae; 16, Camosauria; 17, Spinosauroidea; 20, Allosauroidea; 24, Coelurosauria; 26, Coeluridae; 27, Compsognathinae; 30, Tyrannosauroidea; 34, Maniraptora; 37, Deinonychosauria.
text-fig. 60. Consensus of recent phylogenetic analyses. All recent analyses agree that Theropoda is a monophylum, and within Theropoda, a monophyletic clade etanurae exists. There is further agreement that Tetanurae includes a monophyletic Coelurosauria and that Aves is part of the latter clade. in The interrelationships and evolution of basal theropod dinosaurs
text-fig. 60. Consensus of recent phylogenetic analyses. All recent analyses agree that Theropoda is a monophylum, and within Theropoda, a monophyletic clade etanurae exists. There is further agreement that Tetanurae includes a monophyletic Coelurosauria and that Aves is part of the latter clade.
text-fig. 59. Consensus cladogram of theropod relationships, showing relationships between several theropod taxa common to all recent cladistic analyses. Possible phylogenetic positions of several important problematic taxa are also indicated. in The interrelationships and evolution of basal theropod dinosaurs
text-fig. 59. Consensus cladogram of theropod relationships, showing relationships between several theropod taxa common to all recent cladistic analyses. Possible phylogenetic positions of several important problematic taxa are also indicated.
text-fig. 52. wo selected reduced consensus trees from the analysis of the complete data set. a, after deletion of 'Chilantaisaurus' maortuensis. B, after deletion of Proceratosaurus and Xuanhanosaurus. in The interrelationships and evolution of basal theropod dinosaurs
text-fig. 52. wo selected reduced consensus trees from the analysis of the complete data set. a, after deletion of 'Chilantaisaurus' maortuensis. B, after deletion of Proceratosaurus and Xuanhanosaurus.
text-fig. 53. Strict consensus tree resulting from the analysis of the pruned data matrix with 51 taxa. Numbers at the nodes indicate bootstrap support values in branches that have more than 50 per cent support. The consensus tree is based on 5544 trees of 652 steps (CI 0-42, RI 0-748, RCI 0-314). in The interrelationships and evolution of basal theropod dinosaurs
text-fig. 53. Strict consensus tree resulting from the analysis of the pruned data matrix with 51 taxa. Numbers at the nodes indicate bootstrap support values in branches that have more than 50 per cent support. The consensus tree is based on 5544 trees of 652 steps (CI 0-42, RI 0-748, RCI 0-314).
FIG. 6. A majority-rule consensus gene tree reconstructed from mtDNA cytochrome oxidase 1 in Redescription and Recognition of Etheostoma cyanorum from Blue River, Oklahoma
FIG. 6. A majority-rule consensus gene tree reconstructed from mtDNA cytochrome oxidase 1 (CO1) sequence data obtained from the Barcode of Life Database (BOLD). Maximum-likelihood (ML) and Bayesian trees had identical topologies. Branch lengths are proportional to inferred mutations. Shading of lineages represents samples from E. whipplei (outgroup) in black, E. cyanorum in gray, and E. radiosum in white. ML bootstrap proportions/ Bayesian posterior probabilities are reported. See Data Accessibility for tree file.
Fig. 2 Strict consensus resulting from a in Phylogenetics and historical biogeography of Encyclia (Laeliinae: Orchidaceae) with an emphasis on the E. adenocarpos complex, a new species, and a preliminary species list for the genus
Fig. 2 Strict consensus resulting from a Maximum Parsimony Analysis of Encyclia using a supermatrix of the regions ITS + rpl32-trnL, trnL-F, ycf1. Stars above the nodes are bootstrap support> 70%. The large star indicates the basal node of Encyclia
FIGURE 2. Strict consensus tree derived from a in Hedyotis nanlingensis (Rubiaceae), a new species from South China
FIGURE 2. Strict consensus tree derived from a simultaneous analysis of nrITS and petD, with Bayesian Posterior Probabilities (PP) indicated above branches. The field collection number after the taxon indicates different populations or individuals.
FIGURE 2. Strict consensus tree derived from a in A new species and new section of Viola (Violaceae) from Guangdong, China
FIGURE 2. Strict consensus tree derived from a combined analysis of matK, rpl16 and atpB-rbcL, with bayesian posterior probabilities (>50) in the nodes. Outgroup: Hybanthus concolor. The species in bold face is the new one described in this study. acronyms at the right are first three letters of sections Chamaemelanium, Dischidium, Nomimium and Danxiaviola.
FIGURE 2. Bayesian majority rule consensus tree inferred from the plastid DNA trnL-F in Evolutionary history of the tribe Astereae in the Flora Iranica area: Systematic implications
FIGURE 2. Bayesian majority rule consensus tree inferred from the plastid DNA trnL-F dataset. Numbers abovebranches are posterior probability (PP) and likelihood as well as parsimony bootstrap (BS) values, respectively. Values>50 % are shown.
FIGURE 4. Majority rule consensus tree obtained from a in A new perennial Erysimum species from Turkey, E. nemrutdaghense (Brassicaceae)
FIGURE 4. Majority rule consensus tree obtained from a Bayesian analysis of 27 perennial species based on ITS nrDNA sequences showing the phylogenetic position of Erysimum nemrutdagense. Malcolmia orsiana (DQ357560) and Malcolmia maritima (AM905723) were used as outgroup. Specimens of the new species are shaded in the clade. Posterior probabilities (PP) are given above branches. Thickened branches indicate significant Bayesian posterior probability ≥50. Figure of perennial life forms are according to Feliner (1992) [a: monocarpic perennial, b: polycarpic with axillary flowering shoots, c and d: polycarpic with flowering shoots arising from the rootstock, e: suffruticose].
FIGURE 1 The strict consensus tree resulted from a in Petrocodon asterocalyx, a new species of Gesneriaceae from Guangxi, China
FIGURE 1 The strict consensus tree resulted from a Maximum-parsimony (MP) analysis based on combined trnL-F and ITS sequences of 19 species. Bootstrap values>50% by MP analysis are given below branches. ※ indicates the new species, Petrocodon asterocalyx.
FIGURE 21. Strict consensus tree obtained from the 6 most parsimonious trees obtained from a in A radiation of hydrobiid snails in the caves and streams at Precipitous Bluff southwest Tasmania, Australia (Mollusca: Caenogastropoda: Rissooidea: Hydrobiidae s.l.) ,
FIGURE 21. Strict consensus tree obtained from the 6 most parsimonious trees obtained from a cladistic analysis of the data in Table 13. The unambiguous character changes are listed on each branch (see Table 14 for list of characters). Bootstrap values of more than 50% are included next to the relevant branches on the left.
FIGURE 36. Strict consensus cladogram summarizing 675 in Taxonomic revision and systematics of New Guinea and Oceania pygmy water boatmen (Hemiptera: Heteroptera: Corixoidea: Micronectidae)
FIGURE 36. Strict consensus cladogram summarizing 675 most parsimonious trees recovered from MP analysis of New Guinea micronectid morphology data matrix with all characters mapped over topology (length = 47; CI = 1.0). Numbers in (parentheses) above branches are Bremer support values. Numbers in black circles indicate respective node number discussed in text. Black bars indicate characters; number below bar corresponds to character number listed in Table 13.
FIGURE 1. Bayesian consensus phylogram obtained from 16 in Molecular evidence for the occurrence of the lichen genus Biatora (Lecanorales, Ascomycota) in the Southern Hemisphere
FIGURE 1. Bayesian consensus phylogram obtained from 16 OTUs and ITS gene loci of the Ramalinaceae clade. Cliostomum griffithii was used as outgroup. Support values (in bold) are given as "BP/PP". Biatora from South America is placed within the rufidula clade with a ML bootstrap value of 99% and a posterior probability of 1.0.
FIGURE 1. The maximum likelihood majority rule consensus tree for the analyzed Pseudorobillarda and related taxa. RAxML bootstrap support values above 50 in Morphology and phylogeny of Pseudorobillarda eucalypti sp. nov., from Thailand
FIGURE 1. The maximum likelihood majority rule consensus tree for the analyzed Pseudorobillarda and related taxa. RAxML bootstrap support values above 50% (ML) are given at the nodes. Phylogeny tree is rooted to Schismatomma decolorans.
The State of Serverless Applications: Collection,Characterization, and Community Consensus - Replication Package
<p>The replication package for our article <em>The State of Serverless Applications: Collection,Characterization, and Community Consensus</em> provides everything required to reproduce all results for the following three studies:</p> <ul> <li>Serverless Application Collection</li> <li>Serverless Application Characterization</li> <li>Comparison Study</li> </ul> <p><strong>Serverless Application Collection</strong></p> <p>We collect descriptions of serverless applications from open-source projects, academic literature, industrial literature, and scientific computing.</p> <p><em>Open-source Applications</em></p> <p>As a starting point, we used an existing data set on open-source serverless projects from <a href="https://gupea.ub.gu.se/bitstream/2077/62544/1/gupea_2077_62544_1.pdf">this study</a>. We removed small and inactive projects based on the number of files, commits, contributors, and watchers. Next, we manually filtered the resulting data set to include only projects that implement serverless applications. We provide <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Collection/Open%20source%20filtering.xlsx">a table</a> containing all projects that remained after the filtering alongside the notes from the manual filtering.</p> <p><em>Academic Literature Applications</em></p> <p>We based our search on an <a href="https://doi.org/10.5281/zenodo.1175423">existing community-curated dataset</a> on literature for serverless computing consisting of over 180 peer-reviewed articles. First, we filtered the articles based on title and abstract. In a second iteration, we filtered out any articles that implement only a single function for evaluation purposes or do not include sufficient detail to enable a review. As the authors were familiar with some additional publications describing serverless applications, we contributed them to the community-curated dataset and included them in this study. We provide <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Collection/Academic%20literature%20filtering.xlsx">a table</a> with our notes from the manual filtering.</p> <p><em>Scientific Computing Applications</em></p> <p>Most of these scientific computing serverless applications are still at an early stage and therefore there is little public data available. One of the authors is employed at the German Aerospace Center (DLR) at the time of writing, which allowed us to collect information about several projects at DLR that are either currently moving to serverless solutions or are planning to do so. Additionally, an application from the German Electron Synchrotron (DESY) could be included. For each of these scientific computing applications, we provide a document containing a description of the project and the names of our contacts that provided information for the characterization of these applications.</p> <ul> <li>SC1 Copernicus Sentinel-1 for near-real-time water monitoring</li> <li>SC2 Reprocessing Sentinel 5 Precursor data with ProEO</li> <li>SC3 High-Performance Data Analytics for Earth Observation</li> <li>SC4 Tandem-L exploitation platform</li> <li>SC5 Global Urban Footprint</li> <li>SC6 DESY - High Throughput Data Taking</li> </ul> <p><em>Collection of serverless applications</em></p> <p>Based on the previously described methodology, we collected a diverse dataset of 89 serverless applications from open-source projects, academic literature, industrial literature, and scientific computing. This dataset is can be found <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Characterization/Dataset.xlsx">i</a>n Dataset.xlsx.</p> <p><strong>Serverless Application Characterization</strong></p> <p>As previously described, we collected 89 serverless applications from four different sources. Subsequently, two randomly assigned reviewers out of seven available reviewers characterized each application along 22 characteristics in a structured collaborative review sheet. The characteristics and potential values were defined a priori by the authors and iteratively refined, extended, and generalized during the review process. The initial moderate inter-rater agreement was followed by a discussion and consolidation phase, where all differences between the two reviewers were discussed and resolved. The six scientific applications were not publicly available and therefore characterized by a single domain expert, who is either involved in the development of the applications or in direct contact with the development team.</p> <p><em>Initial Ratings & Interrater Agreement Calculation</em></p> <p>The initial reviews are available as <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Characterization/Initial%20Characterizations.csv">a table</a>, where every application is characterized along with the 22 characteristics. A single value indicates that both reviewers assigned the same value, whereas a value of the form <code>[Reviewer 2] A | [Reviewer 4] B</code> indicates that for this characteristic, reviewer two assigned the value A, whereas reviewer assigned the value B.</p> <p>Our script for the calculation of the Fleiß-Kappa score based on this data is also <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Characterization/CalculateKappa.py">publically available</a>. It requires the python package <code>pandas</code> and <code>statsmodels</code>. It does not require any input and assumes that the file <code>Initial Characterizations.csv</code> is located in the same folder. It can be executed as follows:</p> <pre><code>python3 CalculateKappa.py </code></pre> <p><em>Results Including Unknown Data</em></p> <p>In the following discussion and consolidation phase, the reviewers compared their notes and tried to reach a consensus for the characteristics with conflicting assignments. In a few cases, the two reviewers had different interpretations of a characteristic. These conflicts were discussed among all authors to ensure that characteristic interpretations were consistent. However, for most conflicts, the consolidation was a quick process as the most frequent type of conflict was that one reviewer found additional documentation that the other reviewer did not find.</p> <p>For six characteristics, many applications were assigned the ''Unknown'' value, i.e., the reviewers were not able to determine the value of this characteristic. Therefore, we excluded these characteristics from this study. For the remaining characteristics, the percentage of ''Unknowns'' ranges from 0–19% with two outliers at 25% and 30%. These ''Unknowns'' were excluded from the percentage values presented in the article. As part of our replication package, we provide the raw results for each characteristic including the ''Unknown'' percentages in the form of bar charts.</p> <p>The script for the generation of these bar charts is also <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Characterization/GenerateResultsIncludingUnknown.py">part of this replication package</a>). It uses the python packages <code>pandas</code>, <code>numpy</code>, and <code>matplotlib</code>. It does not require any input and assumes that the file <code>Dataset.csv</code> is located in the same folder. It can be executed as follows:</p> <pre><code>python3 GenerateResultsIncludingUnknown.py </code></pre> <p><em>Final Dataset & Figure Generation</em></p> <p>In the following discussion and consolidation phase, the reviewers compared their notes and tried to reach a consensus for the characteristics with conflicting assignments. In a few cases, the two reviewers had different interpretations of a characteristic. These conflicts were discussed among all authors to ensure that characteristic interpretations were consistent. However, for most conflicts, the consolidation was a quick process as the most frequent type of conflict was that one reviewer found additional documentation that the other reviewer did not find. Following this process, we were able to resolve all conflicts, resulting in a collection of 89 applications described by 18 characteristics. This dataset is available here: <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Characterization/Dataset.xlsx">link</a></p> <p>The script to generate all figures shown in the chapter "Serverless Application Characterization can be found <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Characterization/GenerateFigures.py">here</a>. It does not require any input but assumes that the file <code>Dataset.csv</code> is located in the same folder. It uses the python packages <code>pandas</code>, <code>numpy</code>, and <code>matplotlib</code>. It can be executed as follows:</p> <pre><code>python3 GenerateFigures.py </code></pre> <p><em>Comparison Study</em></p> <p>To identify existing surveys and datasets that also investigate one of our characteristics, we conducted a literature search using Google as our search engine, as we were mostly looking for grey literature. We used the following search term:</p> <pre><code>("serverless" OR "faas") AND ("dataset" OR "survey" OR "report") after: 2018-01-01 </code></pre> <p>This search term looks for any combination of either serverless or faas alongside any of the terms dataset, survey, or report. We further limited the search to any articles after 2017, as serverless is a fast-moving field and therefore any older studies are likely outdated already. This search term resulted in a total of 173 search results. In order to validate if using only a single search engine is sufficient, and if the search term is broad enough, we checked if the seven studies the authors were already familiar with are contained in the search results. As all seven studies were contained in the search results, we concluded that the literature search was broad enough. In a first iteration, we filtered out all results that do not either report original data or report on data from another study. Next, we removed all reports on secondary data, where the original study was already contained in the search results. This process resulted in a total of 16 identified studies. Finally, we determined for each identified study if they investigate one of our characteristics. This resulted in a total of ten related studies. The results from the literature search and the notes from the filtering are part of this replication package as <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Comparison%20Study/ComparisonSearch.xlsx">a table</a>.</p> <p>As these studies use different answer options than our study, we mapped their answer options to ours. In many cases, this was straightforward, such as mapping HTTP to HTTP Request. If the answer option granularities between the studies differed, we aggregated answer options from the study with lower granularity to match the higher granularity study. In case the lower granularity study allowed multiple answers, we selected only the highest value instead of aggregating them, to avoid counting a single study participant multiple times. As this mapping process is somewhat subjective, we provide a detailed account of the mapping for each characteristic and related study <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Comparison%20Study/Comparison%20Mappings.xlsx">as a multi-sheet excel table</a>, where each sheet shows our mapping alongside our notes which answer options were mapped.</p> <p>For many studies, not all information required for traditional meta-analysis techniques, such as cohort size, is available, preventing the application of these meta-analysis techniques. Therefore, we came up with an agreement metric that equally weights the agreement of the reported ranking and the agreement of the reported percentage values. It combines the relative difference between the reported percentages of both studies and the order of the reported popularities of the answer options. We categorize scores in the range [0.8, 1] as very high agreement, [0.6,0.6[ as high agreement, [0.4, 0.6[ as medium agreement, [0.2, 0.4[ as low agreement, and [0, 0.2[ as very low agreement. We acknowledge that these categories are somewhat arbitrary, however, based on a manual inspection of the results, they do seem to capture the level of agreement between the individual studies quite well. Our replication package includes <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Comparison%20Study/Comparison%20Mappings.xlsx">the mapped data</a> alongside the resulting scores to enable a manual inspection of the degree of agreement. The script that implements the calculation of our score is also <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Comparison%20Study/corroboration_analysis.py">publically available</a>. It uses the python packages <code>pandas</code>, <code>numpy</code>, and <code>scipy</code>. The script does not require any input and assumes that the file <code>Comparison Mappings.xlsx</code> is located in the same folder. The script can be executed as follows:</p> <pre><code>python3 corroboration_analysis.py </code></pre> <p>Further, the script to generate all figures shown in the chapter <em>Comparison Analysis</em> is the final piece of our replication package: <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Comparison%20Study/barcharts.py">link</a>. It uses the python packages <code>numpy</code> and <code>matplotlib</code>. It does not require any input and can be executed as follows:</p> <pre><code>python3 barcharts.py </code></pre> <p>If you have any questions about our study or require any additional information/data please contact the first author.</p>
FIGURE 3. The strict consensus tree from parsimony analysis for the combined 16S and RAG1-AmpF1 in Maluti Mystery: A systematic review of Amietia vertebralis (Hewitt, 1927) and Strongylopus hymenopus (Boulenger, 1920) (Anura: Pyxicephalidae)
FIGURE 3. The strict consensus tree from parsimony analysis for the combined 16S and RAG1-AmpF1 dataset with Pyxicephalus adspersus as the outgroup. Bootstrap values are shown at the major nodes. Branch lengths are proportional to the number of unambiguous changes in the original sequence data. Abbreviations of localities for sequenced samples are as follows: M = Mohlaka, B = Bafali, D = Senqu, Q = Qabane, S = Tsatsana, T = Tugela, V = Vemvane, J = Sani, * = Genbank sequence. Note that the two Amietia angolensis sequences from Genbank emerge from the tree as divergent lineages. This may indicate cryptic diversity or simply misidentification of these phenotypically diverse and difficult to identify frogs.
FIGURE 2. Phylogenetic consensus tree among hermit crab species obtained from a in Molecular analysis validates of some informal morphological groups of Pagurus (Fabricius, 1775) (Anomura: Paguridae) from South America
FIGURE 2. Phylogenetic consensus tree among hermit crab species obtained from a fragment of Histone H3 (nDNA), inferred from Maximum Likelihood (ML) Maximum Parsimony (MP) and Neighbor-Joining (NJ) analysis. Topology of a ML is presented, with bootstrap values shown from left to right are for ML, MP and NJ respectively. Support numbers ≤ 50% are shown.
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