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Fig. 8. Consensus trees from phylogenetic analyses under equal weights. A. Strict consensus tree. B. Reduced consensus tree. a in New postcranial remains of large toxodontian notoungulates from the late Oligocene of Mendoza, Argentina and their systematic implications
Fig. 8. Consensus trees from phylogenetic analyses under equal weights. A. Strict consensus tree. B. Reduced consensus tree. a, Martinmiguelia fernandezi; b, Taubatherium paulacoutoi; c, Ancylocoelus frequens; d, Huilatherium pluriplicatum; e, Asmodeus petrasnerus; f, Colpodon antucoensis + C. propinquus.
Fig. 4. A. Strict consensus tree produced from 85 in A re-evaluation of the enigmatic dinosauriform Caseosaurus crosbyensis from the Late Triassic of Texas, USA and its implications for early dinosaur evolution
Fig. 4. A. Strict consensus tree produced from 85 MPTs, each of length 1764 steps. B. Reduced strict consensus tree produced from 91 MPTs, each of length 1764, following the removal of the possible chimera Agnosphitys cromhallenesis. For Bremer support values calculated in this analysis for each of the major nodes, see Table 1. Studied specimens in bold.
Fig. 4. Strict consensus cladogram resulting from a in A new plesiosauroid from the Toarcian (Lower Jurassic) of Alhadas, Portugal
Fig. 4. Strict consensus cladogram resulting from a reanalysis of the data matrix of Grossmann (2007), with Lusonectes included as an additional operational taxonomic unit. See text for interpretation. SMNS16812 is the holotype of "Plesiopterys wildii" = Seeleyosaurus according to Grossman (2007).
FIGURE 7. 1, Strict consensus tree for parsimony analysis including Marada arcanum. 2 in New genus of primitive wombat (Vombatidae, Marsupialia) from Miocene deposits in the Riversleigh World Heritage Area (Queensland, Australia)
FIGURE 7. 1, Strict consensus tree for parsimony analysis including Marada arcanum. 2, Bootstrap support for single most parsimonious tree from analysis excluding Marada arcanum (see text for details). Vombatidae indicated in purple.
FIG. 3. — Bayesian consensus tree inferred from concatenated chloroplast rps4 in Pterygoneurum sampaianum (Guim.) Guim.: range extension to Africa, first mentions in France, confirmation of specific status and improved morphological circumscription
FIG. 3. — Bayesian consensus tree inferred from concatenated chloroplast rps4-trnS and trnM-trnV sequence data of the analysed dataset of Pottiaceae subfam. Pottioideae, partitioned between DNA sequence and indel data. Posterior probability from BI is displayed above the branches; bootstrap support (656 replications) from ML analysis is displayed below the branches.
Fig. 4. A. Strict consensus trees for 8638 trees 413 in Phylogenetic position of the crocodylian Megadontosuchus arduini and tomistomine palaeobiogeography
Fig. 4. A. Strict consensus trees for 8638 trees 413 steps long derived from the analysis including Megadontosuchus arduini. The geographic range of tomistomines is abbreviated as follows: CE, Central Europe; EA, Eastern Asia; NA, North America; NAf, Northern Africa; SEA, South Eastern Asia; WE, Western Europe; NWE, North Western Europe. Consensus statistics: tree length (L) = 430; consistency index (CI) = 0.4581; homoplasy index (HI) = 0.5419; CI excluding uninformative characters = 0.4261; HI excluding uninformative characters = 0.5739; retention index (RI) = 0.7272; rescaled consistency index (RC) = 0.3331. B. Strict consensus topology for 4344 trees 409 steps long derived from the analysis excluding M. arduini. Consensus statisics: L = 416; CI = 0.4712; HI = 0.5288; CI excluding uninformative characters = 0.4388; HI excluding uninformative characters = 0.5612; RI = 0.7406; RC = 0.3489. C. Adams consensus tree derived from the 47 taxa matrix.
Advancing Robotic Swarms with Blockchain Technology: A Dynamic Two-Factor Authentication Consensus Framework
<h1><strong><span>Data Description and File Structure:</span></strong></h1> <p>This data repository contains the raw data collected across all the experiments describe from the paper entitled “Advancing Robotic Swarms with Blockchain Technology: A Dynamic Two-Factor Authentication Consensus Framework”. These are available as CSV files under the appropriate directories.</p> <p>Three main folders are found in this repository:</p> <ul> <li><code><strong>1FA-single-factor-auth/</strong></code> <ul> <li>Contains raw data from experiments using the Single-Factor Authentication (1FA) framework, where only on-chain consensus validation (OCV) is applied without the off-chain peer verification (OPV) phase.</li> </ul> </li> <li><code><strong>2FBC_two-factor-blockchain/</strong></code> <ul> <li>Includes data from experiments employing the Two-Factor Blockchain Consensus (2FBC) framework, which integrates both off-chain peer verification (OPV) and on-chain consensus validation (OCV) phases for enhanced security. This also contains the baseline results.</li> </ul> </li> <li><code><strong>BB_blockchain-base/</strong></code> <ul> <li>Stores the experimental data from the Blockchain Base (BB) framework, where a basic blockchain model was used without the multi-factor authentication features of 1FA or 2FBC. Most data points here are obtained from the work of Strobel et al. (2023) in their work, <u>doi/10.1126/scirobotics.abm4636</u></li> </ul> </li> </ul> <p>Under each directory, we have the following folders:</p> <ul> <li><code><strong>exp_1/</strong></code> <ul> <li>Contains data from scalability experiments, where swarm size was increased within a fixed 3.6 m² arena to evaluate the framework’s performance as the number of robots grows.</li> </ul> </li> <li><code><strong>exp_2/</strong></code> <ul> <li>Includes data from accuracy tests that varied the percentage of white tiles in the environment to assess the framework's ability to reach accurate consensus under different conditions.</li> </ul> </li> <li><code><strong>exp_3a/</strong></code> <ul> <li>Stores data from robustness experiments focused on testing the swarm's resilience to different numbers of Byzantine robots within the network.</li> </ul> </li> <li><code><strong>exp_3b/</strong></code> <ul> <li>Contains data from experiments evaluating the robustness of the swarm when subjected to various Byzantine attack types, testing the framework’s ability to handle adversarial behaviors.</li> </ul> </li> <li><code><strong>exp_4/</strong></code> <ul> <li>Holds data from the resource efficiency experiments, which measured the computational resource usage (CPU, RAM, and blockchain size) during a prolonged 10-hour swarm operation.</li> </ul> </li> </ul> <p>Each experiment configuration is carried out in 20 repetitions.</p> <h3><strong><em>Experiment 1 (exp_1):</em></strong></h3> <ul> <li><code><strong>8rob-2byz/</strong></code> Data for scalability experiments with 8 robots, 2 of which are Byzantine.</li> <li><code><strong>16rob-4byz/</strong></code> Data for scalability experiments with 16 robots, 4 of which are Byzantine.</li> <li><strong><code>24rob-6byz/</code> </strong>Data for scalability experiments with 24 robots, 6 of which are Byzantine.</li> <li><code><strong>48rob-12byz/</strong></code> Data for scalability experiments with 48 robots, 12 of which are Byzantine.</li> </ul> <h3><strong><em>Experiment 2 (exp_2):</em></strong></h3> <ul> <li><code><strong>24rob-5floor-6byz/</strong></code> Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 5% white floor tiles.</li> <li><code><strong>24rob-25floor-6byz/</strong></code> Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 25% white floor tiles.</li> <li><strong><code>24rob-45floor-6byz/</code> </strong>Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 45% white floor tiles.</li> <li><strong><code>24rob-75floor-6byz/</code> </strong>Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 75% white floor tiles.</li> </ul> <h3><strong><em>Experiment 3a (exp_3a):</em></strong></h3> <ul> <li><strong><code>24rob-0byz/</code> </strong>Data for robustness experiments with 24 robots and no Byzantine robots.</li> <li><strong><code>24rob-3byz/</code> </strong>Data for robustness experiments with 24 robots and 3 Byzantine robots.</li> <li><code><strong>24rob-6byz/</strong></code> Data for robustness experiments with 24 robots and 6 Byzantine robots.</li> <li><strong><code>24rob-9byz/</code> </strong>Data for robustness experiments with 24 robots and 9 Byzantine robots.</li> </ul> <h3><strong><em>Experiment 3b (exp_3b):</em></strong></h3> <ul> <li><strong><code>24rob-6byz-1style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 1 or 0% white tile estimate</li> <li><strong><code>24rob-6byz-2style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 2 or 100% white tile estimate</li> <li><strong><code>24rob-6byz-3style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 3 or attack from a Bernoulli distribution</li> <li><code><strong>24rob-6byz-4style/</strong></code> Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 4 or attack from a Uniform distribution</li> <li><code><strong>24rob-6byz-5style/</strong></code> Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 5 or flooding</li> <li><strong><code>24rob-6byz-6style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 6 or eavesdropping</li> </ul> <h3><strong><em>Experiment 4 (exp_4):</em></strong></h3> <ul> <li><strong><code>8rob-2byz/</code> </strong>Data for resource efficiency experiments with 8 robots, 2 of which are Byzantine.</li> <li><strong><code>16rob-4byz/</code> </strong>Data for resource efficiency experiments with 16 robots, 4 of which are Byzantine.</li> <li><code><strong>24rob-6byz/</strong></code> Data for resource efficiency experiments with 24 robots, 6 of which are Byzantine.</li> <li><code><strong>48rob-12byz/</strong></code> Data for resource efficiency experiments with 48 robots, 12 of which are Byzantine.</li> <li><strong><code>72rob-18byz/</code> </strong>Data for resource efficiency experiments with 72 robots, 18 of which are Byzantine.</li> <li><strong><code>96rob-24byz/</code> </strong>Data for resource efficiency experiments with 96 robots, 24 of which are Byzantine.</li> <li><strong><code>120rob-30byz/</code> </strong>Data for resource efficiency experiments with 120 robots, 30 of which are Byzantine.</li> </ul> <h3><strong>Relevant Files:</strong></h3> <ul> <li><code><strong>block.csv</strong></code> Contains information about each blockchain block generated during the experiment, including block number, size, timestamp, and the number of transactions. The TELAPSED column indicates the time elapsed since the previous block was generated.</li> <li><code><strong>estimate.csv</strong></code> Stores the estimates collected by each robot during the simulation. Each entry includes the time of the estimate and the estimated percentage of white tiles in the arena.</li> <li><code><strong>sc.csv</strong></code> Contains information on smart contract interactions, including the mean estimate across robots, vote counts, and whether consensus was achieved (C?).</li> <li><strong><code>extra.csv</code></strong> Records additional performance metrics during the experiments, including CPU and RAM usage, as well as the size of the blockchain data folder.</li> </ul> <h3><strong>Relevant Data Fields:</strong></h3> <ul> <li><code><strong>ID</strong></code> The identifier assigned to each robot participating in the experiment. It remains constant across all entries for a particular robot.</li> <li><code><strong>TIME</strong></code> The timestamp (in seconds) at which the data was recorded. This is relative to the start of the simulation.</li> <li><code><strong>TELAPSED</strong></code> Indicates the time elapsed between blocks or events, recorded in seconds.</li> <li><code><strong>TIMESTAMP </strong></code>Represents the Unix timestamp when a blockchain block was generated, denoting the actual system time.</li> <li><code><strong>BLOCK </strong></code>The blockchain block number created by the system during the simulation. This value increments as new blocks are added.</li> <li><code><strong>SIZE</strong></code> The size of each block in bytes, indicating the data storage requirement of each blockchain entry.</li> <li><strong><code>ESTIMATE</code> </strong>The estimate provided by the robot, representing the percentage of white tiles detected in the arena.</li> <li><strong><code>MEAN</code> </strong>The mean estimate across the swarm, as calculated on-chain via the smart contract.</li> <li><code><strong>VOTECOUNT</strong></code> Total number of estimates submitted to the smart contract for consensus validation.</li> <li><code><strong>VOTEOKCOUNT</strong></code> The number of valid votes that passed the validation process (e.g., not flagged as outliers).</li> <li><strong><code>C?</code> </strong> A Boolean value indicating whether consensus has been achieved for a given block of estimates.</li> <li><code><strong>CPU</strong></code> Percentage of CPU utilization, showing the computational load on the robot during the simulation.</li> <li><code><strong>RAM</strong></code> The amount of RAM used by each robot during the experiment, measured in percent or bytes.</li> <li><code><strong>KB</strong></code> The size of the blockchain data folder, measured in kilobytes (KB). This indicates how much data was stored by the blockchain system during the experiment.</li> </ul>
XRP Ledger Consensus Protocol Debug-level Log Traces
<p>A dataset of log traces from the consensus protocol of a <a href="https://github.com/ripple/rippled">rippled server</a> instance. The traces are filtered at the debug (DBG) level. Each file contains a separate trace, representing a full round of the consensus protocol.</p>
XRP Ledger Consensus Protocol Traces Dataset
<p>This artifact contains a dataset of log traces produced by the XRP Ledger Consensus Protocol at the debug level (DBG). Each file represents a log traces consisting of the logs produced during one round of the Consensus Protocol.</p>
Figure 2. Majority rule consensus tree for the 16S in Genus Baseodiscus (Nemertea: Heteronemertea): Molecular identification of a new species in a phylogenetic context
Figure 2. Majority rule consensus tree for the 16S rRNA data resulting from the Bayesian analysis (model GTR+G+I), 1,000,000 generations. Numbers refer to posterior probabilities.
Figure 6. A Bayesian consensus tree derived from 4,001 in A Heron (Aves: Ardeidae) from the Early Miocene St Bathans Fauna of Southern New Zealand
Figure 6. A Bayesian consensus tree derived from 4,001 trees sampled: Run 1 (mean = –546.302, s.d. = 0.099, Effective Sample Size = 3208.424); Run 2 (–546.223, 0.113, 2388.97). Support values are shown above the corresponding node.
Fig. 4. Consensus tree for the combined 28S in On ''Molecular Phylogeny of Vespidae (Hymenoptera) and the Evolution of Sociality in Wasps''
Fig. 4. Consensus tree for the combined 28S data and the morphological and behavioral characters (see appendix 1). The length is of the two underlying cladograms is 458 steps; consistency index = 0.81 and retention index = 0.85.
Text-fig. 10. Phylogenetic relationships of Miocene hyaenodonts (for definitions of character states see Table 2). The data matrix was compiled in MacClade 4.05 and run in PAUP 4.0b10 (Macintosh version). We chose Cimolestes magnus CLEMENS et RUSSELL, 1965, (additional data from Lillegraven 1969), as the outgroup. The unordered and unweighted analysis produced 16 trees. a: Majority-rule consensus. b: Strict consensus. Consistency index (CI): 0.5882; Homoplasy index (HI): 0.4118; Retention index (RI): 0.7742. in New Hyaenodonts (Ferae, Mammalia) From The Early Miocene Of Napak (Uganda), Koru (Kenya) And Grillental (Namibia)
Text-fig. 10. Phylogenetic relationships of Miocene hyaenodonts (for definitions of character states see Table 2). The data matrix was compiled in MacClade 4.05 and run in PAUP 4.0b10 (Macintosh version). We chose Cimolestes magnus CLEMENS et RUSSELL, 1965, (additional data from Lillegraven 1969), as the outgroup. The unordered and unweighted analysis produced 16 trees. a: Majority-rule consensus. b: Strict consensus. Consistency index (CI): 0.5882; Homoplasy index (HI): 0.4118; Retention index (RI): 0.7742.
Fig. 22. Strict consensus tree from 2004 in Redescription and Phylogenetic Position of the Early Miocene Penguin Paraptenodytes antarcticus from Patagonia
Fig. 22. Strict consensus tree from 2004 trees of 148 steps, osteological dataset. Absolute Bremer values are above branches, relative values are below branches. Bremer values were calculated from a sample of 16,000 trees (see Methods). CI = 0.60, RI = 0.90.
Fig. 76. Results from exploratory phylogenetic analysis including Epidendrosaurus ningchengensis. A, reduced strict consensus cladogram. B in A Review Of Dromaeosaurid Systematics And Paravian Phylogeny
Fig. 76. Results from exploratory phylogenetic analysis including Epidendrosaurus ningchengensis. A, reduced strict consensus cladogram. B, detail of the base of Avialae showing the number of additional steps required to constrain Epidendrosaurus and Epidexipteryx are sister taxa.
Fig. 63. Strict consensus cladogram illustrating the troodontid relationships. Dromaeosaurid taxa have been collapsed into a in A Review Of Dromaeosaurid Systematics And Paravian Phylogeny
Fig. 63. Strict consensus cladogram illustrating the troodontid relationships. Dromaeosaurid taxa have been collapsed into a single terminal.
Fig. 62. Reduced strict consensus cladogram illustrating the relationships among nonparavian maniraptorans. Paravian taxa have been collapsed into a in A Review Of Dromaeosaurid Systematics And Paravian Phylogeny
Fig. 62. Reduced strict consensus cladogram illustrating the relationships among nonparavian maniraptorans. Paravian taxa have been collapsed into a single terminal.
Figure 2. Strict consensus cladogram from two equally parsimonious trees from a in Notograptidae, sister to Acanthoplesiops Regan (Teleostei: Plesiopidae: Acanthoclininae), with comments on biogeography, diet and morphological convergence with Congrogadinae (Teleostei: Pseudochromidae)
Figure 2. Strict consensus cladogram from two equally parsimonious trees from a branch-and-bound search using composite coding of the data set in Table 1 (no. of steps = 123; CI = 0.642; RC = 0.547; RI = 0.852). Nodes are lettered as in the text. Characters supporting each node, those without homoplasy in bold, are: A – 1, 2, 3, 4(1), 5, 6, 7, 8; B – 9, 10(1), 11(1); C – 12, 13, 14, 15, 16; D – 17; E – 18, 19(1), 20; F – 21, 22, 23; G – 10(2), 24, 25, 26; H – 4(2), 18, 19(1), 28, 29, 30, 31(1); I – 32(1), 33; J – 46, 58; K – 34, 35, 36, 37, 38(1); L – 19(2), 39, 40(1), 41, 42, 46, 47; M – 43, 44, 45; N – 31(2), 48, 50, 51, 53; O – 32(3), 49, 52, 54, 55, 56, 57, 59; P – 47; Q – 36, 38(2). Numbers below nodes are decay indices.
Figure 4. 18S rRNA strict consensus tree from 12 in Reconstructing the Anomalodesmata (Mollusca: Bivalvia): morphology and molecules
Figure 4. 18S rRNA strict consensus tree from 12 most parsimonious trees (3228 steps, CI = 0.4786, RC = 0.3076). Above branches are 'bootstrap proportion | decay index', below are 'Bayesian posterior probability | ML-puzzling proportion'. Arrows indicate the 'thraciid' (T) and 'lyonsiid' (L) lineages.
Fig. 4. Maximum Parsimony consensus tree for the mitochondrial gene Cytochrome Oxidase I in New species of Moenkhausia Eigenmann, 1903 (Characiformes: Characidae) with comments on the Moenkhausia oligolepis species complex
Fig. 4. Maximum Parsimony consensus tree for the mitochondrial gene Cytochrome Oxidase I. Numbers represent values of 1000 bootstrap replicates.
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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.