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FIGURE 6. Capitonius bicolor used for making description. A in New taxonomic status of Foenomorpha Szépligeti as a subgenus of Capitonius Brullé (Hymenoptera: Braconidae: Cenocoeliinae) including a revision of species
FIGURE 6. Capitonius bicolor used for making description. A, anterior head; B, lateral head; C, dorsal head; D, dorsal mesosoma; E, lateral mesosoma; F, dorsal metasoma; G, forewing.
Healthy Aging Is Associated With Decreased Risk-Taking in Motor Decision-Making
<p>The Zip file contains all data relative to the publication.</p> <p>Data files are identified as follows:</p> <p>Part[Y/O for Younger/Older]Sess[Session Number]</p> <p>A description of the variables is contained in the file VARIABLE CODES.txt</p> <p> </p>
Fig. 1. Joe McHugh makes a in Minutes of the Annual Meeting Knoxville, Tennessee, 13 November 2012
Fig. 1. Joe McHugh makes a point during his erudite presentation on the classification of Cucujoidea.
Figs. 25–33 in Colydiine Genera (Coleoptera: Zopheridae: Colydiinae) of the New World: A Key and Nomenclatural Acts 30 Years in the Making
Figs. 25–33. Dorsal habitus of New World colydiine genera, tribe Synchitini. 25) Endeitoma dentata; 26) Eucicones marginalis; 27) Eudesma undulata; 28) Globotrichus harti; 29) Helonoton ashei; 30) Holopleuridia sp., Panama; 31) Lasconotus complex; 32) Lobogestoria gibbicollis; 33) Lyreus alleni.
Figs. 1–2. Colydiinae anatomy. 1 in Colydiine Genera (Coleoptera: Zopheridae: Colydiinae) of the New World: A Key and Nomenclatural Acts 30 Years in the Making
Figs. 1–2. Colydiinae anatomy. 1) Antennal types. Club 1-segmented: a) Endeitoma sp., b) Synchita sp., c) Acolobicus sp. Club 2-segmented: d) Colobicus sp., e) Lobogestoria sp. Club 3-segmented: f) Lasconotus sp., g) Colydium sp., h) Rhagodera sp. Club gradual: i) Endestes sp.; 2) Tarsi, prothoracic leg. a) Nematidium sp., b) Lobogestoria sp., c) Pseudaulonium sp., d) Acropis sp., e) Monoedus sp.
Figs. 52–53 in Colydiine Genera (Coleoptera: Zopheridae: Colydiinae) of the New World: A Key and Nomenclatural Acts 30 Years in the Making
Figs. 52–53. Dorsal habitus of New World colydiine genera, tribe Synchitini and incertae sedis. 52) Synchita fuliginosa; 53) Phreatus rigidus.
Figs. 43 –51 in Colydiine Genera (Coleoptera: Zopheridae: Colydiinae) of the New World: A Key and Nomenclatural Acts 30 Years in the Making
Figs. 43 –51. Dorsal habitus of New World colydiine genera, tribe Synchitini. 43) Pharax laticollis; 44) Phloeodalis raucus; 45) Phloeonemus integer; 46) Pristoderus regularis; 47) Pseudocorticus blairi; 48) Rapthius peruvianus; 49) Reylus chilensis; 50) Slipinskius chilensis; 51) Stenomonoedus garleppi.
Figs. 5–6. Colydiinae anatomy. 5 in Colydiine Genera (Coleoptera: Zopheridae: Colydiinae) of the New World: A Key and Nomenclatural Acts 30 Years in the Making
Figs. 5–6. Colydiinae anatomy. 5) Heads, ventral view, illustrating the antennal groove. a) Antennal groove absent, b) Antennal groove short, not reaching midpoint of eye, c) Antennal groove long, reaching past midpoint of eye; 6) Generalized elytron, illustrating the alternating intervals and interstriae.
Figs. 3–4. Colydiinae anatomy. 3 in Colydiine Genera (Coleoptera: Zopheridae: Colydiinae) of the New World: A Key and Nomenclatural Acts 30 Years in the Making
Figs. 3–4. Colydiinae anatomy. 3) Prothoraces, ventral view, illustrating procoxal cavity closure (external). a) Broadly open (Holopleuridia sp.), b) Narrowly open (Monoedus sp.), c) Moderately closed (Lasconotus sp.), d) Narrowly closed (Lobogestoria sp.), e) Broadly closed (Nematidium sp.); 4) Abdomina, ventral view. a) Intercoxal process acute, ventrites free, sutures flat, ventrite V with subapical groove, b) Intercoxal process broadly rounded, ventrites I–III connate, c) Intercoxal process truncate, ventrites free, sutures deeply impressed.
Figs. 16 –24 in Colydiine Genera (Coleoptera: Zopheridae: Colydiinae) of the New World: A Key and Nomenclatural Acts 30 Years in the Making
Figs. 16 –24. Dorsal habitus of New World colydiine genera, tribes Rhagoderini and Synchitini. 16) Rhagodera texana; 17) Acolobicus erichsoni; 18) Antilissus aper; 19) Asynchita granosa; 20) Bitoma crenata; 21) Colobicus parilis; 22) Colydodes gibbiceps; 23) Coxelus serratus; 24) Denophoelus nosodermoides.
Figs. 34 –42 in Colydiine Genera (Coleoptera: Zopheridae: Colydiinae) of the New World: A Key and Nomenclatural Acts 30 Years in the Making
Figs. 34 –42. Dorsal habitus of New World colydiine genera, tribe Synchitini. 34) Megataphrus tenuicornis; 35) Microprius rufulus; 36) Monoedus guttatus; 37) Namunaria guttulata; 38) Neotrichus verrucatus; 39) Notocoxelus sp., Chile; 40) Paha laticollis; 41) Paryphus serratus; 42) Paxillobitoma clinei.
Figs. 7–15 in Colydiine Genera (Coleoptera: Zopheridae: Colydiinae) of the New World: A Key and Nomenclatural Acts 30 Years in the Making
Figs. 7–15. Dorsal habitus of New World colydiine genera, tribes Acropini, Colydiini, Gempylodini, and Nematidiini. 7) Acropis maracapatana; 8) Ethelema costaricensis; 9) Plagiope tuberculata; 10) Anarmostes ater; 11) Aulonium longum; 12) Colydium lineola; 13) Pseudaulonium sp., Venezuela; 14) Endestes incilis; 15) Nematidium filiforme.
Double dissociation of dopamine and subthalamic nucleus stimulation on effortful cost/benefit decision making
Open the record for dataset details and reuse information.
Dataset for "Activation of the mPFC-NAc pathway reduces motor impulsivity but does not affect risk-related decision-making in innately high-impulsive male rats"
<p><span>Attention-deficit/hyperactivity disorder (ADHD) and substance use disorders</span><span> (SUD) are characterized by exacerbated motor and risk-related impulsivities, which are associated with decreased cortical activity. In rodents, the medial prefrontal cortex (mPFC) and nucleus accumbens (NAc) have been separately implicated in impulsive behaviors, but studies on the specific role of the mPFC-NAc pathway in these behaviors are limited. Here, we investigated whether heightened impulsive behaviors are associated with reduced mPFC activity in rodents, and determined the involvement of the mPFC-NAc pathway in motor and risk-related impulsivities. We used the Roman High- (RHA) and Low-Avoidance (RLA) rat lines, which display divergent phenotypes in impulsivity. To investigate alterations in cortical activity in relation to impulsivity, regional brain glucose metabolism was measured using positron emission tomography and [<sup>18</sup>F]-fluorodeoxyglucose ([<sup>18</sup>F]FDG). Using chemogenetics, the activity of the mPFC-NAc pathway was either selectively activated in high-impulsive RHA rats or inhibited in low-impulsive RLA rats, and the effects of these manipulations on motor and risk-related impulsivity were concurrently assessed using the rat gambling task. We showed that basal [<sup>18</sup>F]FDG uptake was lower in the mPFC and NAc of RHA compared to RLA rats. Activation of the mPFC-NAc pathway in RHA rats reduced motor impulsivity, without affecting risk-related decision-making. Conversely, inhibition of the mPFC-NAc pathway had no effect in RLA rats. Our results suggest that the mPFC-NAc pathway controls motor impulsivity, but has limited involvement in risk-related decision-making. Our findings suggest that reducing fronto-striatal activity may help attenuate motor impulsivity in patients with impulse control dysregulation like ADHD or SUD.</span></p>
Replication package: Temperature and decision-making
<p>Michelle Escobar Carias, David Johnston, Rachel Knott, Rohan Sweeney (2024). Replication package: Temperature and decision-making</p>
Replication Package of the Paper "How do Papers Make into Machine Learning Frameworks: A Preliminary Study on TensorFlow"
<p>This replication package contains datasets and scripts related to the paper: "<em>How do Papers Make into Machine Learning Frameworks: A Preliminary Study on TensorFlow</em>"</p> <ul> <li> <p><code>Contributor_Classification.csv</code>: contains the assignment of each contributor to a specific classification. The file contains the following columns:</p> <ul> <li><em>Date</em> : contains the date of each comment</li> <li><em>Type</em>: describe the type of a pull request (if it is Close, Commit, DESCR, Merge, PC, RC)</li> <li><em>ID</em> : specific ID of the comment</li> <li><em>Body</em> : contains the body of the comment analyzed</li> <li><em>Url</em> : link at each comments</li> <li><em>NumberPR</em> : number of a PR</li> <li><em>Name_Contributor</em>: contains the name of a contributor for each comment</li> <li><em>Contributor_classification</em>: contains the assignment of a specific classification (academic, bot, ML expert, software engineer, unknown), obtained after manual analysis, for each contributor of a comment</li> </ul> </li> <li> <p><code>Contributors_ManualAnalysis.csv</code>: contains the manual analysis performed by two authors to assign a classification for each contributor. The file contains the following columns:</p> <ul> <li><em>Contributors</em>: contains the name of the contributor for each comment</li> <li><em>Link GitHub</em>: contains the link to the GitHub page for each contributor</li> <li><em># PR</em>: contains the number of PRs in which a specific contributor is involved</li> <li><em>Annotator1</em>: manual classification of the first annotator</li> <li><em>Annotator2</em>: manual classification of the second annotator</li> <li><em>Final Classification</em>: contains the final label (academic, bot, ML expert, software engineer, unknown)assigned for each contributor</li> <li><em>Organization</em>: contains the organization, if any (Google, Hugging Face, Microsoft, OpenAI)</li> </ul> </li> <li> <p><code>ManualAnalysis.csv</code>: contains the manual analysis performed regarding Comment Type, Nature of Comment and Artifact. The .csv contains the following columns:</p> <ul> <li><em>URL</em>: contains the link to each comment</li> <li><em>NumberPR</em> : number of pull request</li> <li><em>CommentType1</em>: contains the classification of the first annotator in merit of comment type (Conventional review, Initial implementation, Management, ML review, Other)</li> <li><em>CommentType2</em>: contains the classification of the second annotator in merit of comment type (Conventional review, Initial implementation, Management, ML review, Other)</li> <li><em>NatureComment1</em>: contains the classification of the first annotator about the nature of the comment (Approval, Bug fix, Buid error, Clarification, Code, Code convention and spacing, Code review, Comment, Enhancement request, Explanation, Feedback, Introducing alternative implementation, Pinging, Plan for merging into TF, Question, References and referrals, Request a review, Request documentation improvement, Request test, Request verification, Review, Review assignment)</li> <li><em>NatureComment2</em>: contains the classification of the second annotator about the nature of the comment (Approval, Bug fix, Buid error, Clarification, Code, Code convention and spacing, Code review, Comment, Enhancement request, Explanation, Feedback, Introducing alternative implementation, Pinging, Plan for merging into TF, Question, References and referrals, Request a review, Request documentation improvement, Request test, Request verification, Review, Review assignment)</li> <li><em>Artifact1</em>: contains the classification of the first annotator with respect to the artifact (Article, Code, Commit, Issue/bug, Link, Review, Other)</li> <li><em>Artifact2</em>: contains the classification of the second annotator with respect to the artifact (Article, Code, Commit, Issue/bug, Link, Review, Other)</li> <li><em>FINALCommentType</em>: contains the final classification of the comment type after the resolution of the conflicts</li> <li><em>FINALNatureComment</em>: contains the final classification of the nature of the comment after resolution of conflicts</li> <li><em>FINALArtifact</em>: contains the final classification of the artifact after the resolution of conflicts</li> </ul> </li> <li> <p><code>Summary_PR.csv</code>: contains the details about the composition of each PRs. The file contains the following columns:</p> <ul> <li><em>#PullRequest</em>: contains the number of all pull requests analyzed</li> <li><em>#events</em>: contains the number of all the events analyzed for each PR</li> <li><em>#comments</em>: contains the number of all comments for each PR</li> <li><em>#Commit</em>:contains the number of Commit for each PR</li> <li><em>PC</em>: contains the number of PC for each PR</li> <li><em>RC</em>: contains the number of RC for each PR</li> </ul> </li> <li> <p><code>Total_PR_Comments.csv</code>: contains information about all comments analyzed. The columns are:</p> <ul> <li><em>Date</em>: contains the date of each comment</li> <li><em>Type</em>: describes the type of a pull request (if it is Close, Commit, DESCR, Merge, PC, RC)</li> <li><em>ID</em>: SHAn of the comment</li> <li><em>NumberPR</em>: PR number</li> <li><em>Name_Contributor</em>: contains the name of a contributor for each comment</li> <li><em>Body</em>: contains the body of the comment analyzed</li> <li><em>Url</em>: link at each comment</li> </ul> </li> </ul> <p>The replication also contains a directory <code>results</code> in which there are quantitative results. The directory contains:</p> <ul> <li> <p><code>Artifacts.csv</code>: This file contains the results for the artifact. The columns are:</p> <ul> <li><em>#PullRequest</em>: number of the pull request analyzed</li> <li><em>Article</em>: contains the percentage of the occurrences of the article in the specific pull request</li> <li><em>Code</em>: contains the percentage of the occurrences of the code in the specific pull request</li> <li><em>Commit</em>: contains the percentage of the occurrences of the commit in the specific pull request</li> <li><em>Issue reference</em>: contains the percentage of the occurrences of the issue reference in the specific pull request</li> <li><em>External link</em>: contains the percentage of the occurrences of the external link in the specific pull request</li> <li><em>Review</em>: contains the percentage of the occurrences of the review in the specific pull request</li> <li><em>Other</em>: contains the percentage of the occurrences of the other in the specific pull request</li> </ul> <p>ßAlso, the <code>Mean_Value</code> row contains the mean value of all occurrences for each column (Article, Code, Commit, Issue reference, External link, Review, Other)</p> </li> <li> <p><code>CommentType.csv</code>: this file contains the results for the comment type. The columns are:</p> <ul> <li><em>#PullRequest</em>:number of the pull request analyzed</li> <li><em>Conventional review</em>: contains the percentage of the occurrences of the conventional review in the specific pull request</li> <li><em>Initial implementation</em>: contains the percentage of the occurrences of the initial implementation in the specific pull request</li> <li><em>Management</em>: contains the percentage of the occurrences of the management in the specific pull request</li> <li><em>ML review</em>: contains the percentage of the occurrences of the ML review in the specific pull request</li> <li><em>Other</em>: contains the percentage of the occurrences of the Other in the specific pull request</li> </ul> <p>Also, the <code>Mean_Value</code> row contains the mean value of all occurrences for each column (Conventional review, Initial implementation, Management, ML review, Other)</p> </li> <li> <p><code>Contributor.csv</code>: this file contains the results for the contributors. The columns are:</p> <ul> <li><em>#PullRequest</em>: number of the pull requests analyzed</li> <li><em>academic</em>: contains the percentage of the occurrences of the academic in the specific pull request</li> <li><em>bot</em>: contains the percentage of the occurrences of the bot in the specific pull request</li> <li><em>ML expert</em>: contains the percentage of the occurrences of the ML expert in the specific pull request</li> <li><em>software engineer</em>: contains the percentage of the occurrences of the software engineer in the specific pull request</li> <li><em>unknown</em>: contains the percentage of the occurrences of unknown in the specific pull request</li> </ul> <p>Also, the <code>Mean_Value</code> row contains the mean value of all occurrences for each column (academic, bot, ML expert, software engineer, unknown)</p> </li> </ul>
Uncertainty in Pedestrian Decision-Making in Urgent Scenarios Modulates Multi-Level Neural Hierarchies from Perception to Execution
<p><span>In urgent traffic scenarios, pedestrians exhibit decision-making uncertainty, significantly influencing safe interaction dynamics with automated vehicles. However, the inherent mechanisms of such decision behavior remain inadequately understood. To address this gap, we designed dynamic interactive stimulus experiments to replicate pedestrian-vehicle interactions in urgent scenarios, incorporating spatiotemporal pressure and introducing substantial penalties for decision failures. We employed multimodal data analysis, including behavioral data, electroencephalography (EEG) and eye-tracking data, to investigate the influence of urgency on uncertainty in decision-making and the underlying multi-level neural processes. Our findings demonstrate that as the urgency of the stimulus increases, humans adjust their decision objectives, resulting in an initial decrease followed by an increase in decision uncertainty when dealing with more urgent stimuli. Specifically, urgency augments top-down perceptual processes during the early perception stage. <span>Such a mechanism implies an enhanced dependence on prior experiences for perceptual </span></span><span><span><span>decision<span>-making in high-urgency situations. </span></span></span></span><span>While urgency accelerated motion preparation time during the decision-execution stage, it is noteworthy that the culmination of evidence accumulation (represented by the CPP peak) manifested later than the actual response. These results suggest that insufficient perceptual information and evidence accumulation may increase decision-making uncertainty. Our experimental study unveils a correlation between human decision-making uncertainty and scenario urgency, particularly within a defined urgency range. </span></p>
Additional Material for: Debiasing Architectural Decision-Making: An Experiment With Students and Practitioners
<p>Additional Material for: Debiasing Architectural Decision-Making: An Experiment With Students and Practitioners submitted to ICSA 2025.<br><br>The files contain:<br>1. README.md - A short file describing the contents of the replication package.<br>2. Coding guide.docx - a guide used for coding, with examples from the experiment's participants.<br>3. Coding_results.xlsx - data with all measurements for all participants.<br>4. Questionnaire.xlsx - questionnaire used to gather demographic participant's data.<br>5. Experiment-plan.docx - the detailed plan of all the experiment steps.<br>6. Debiasing-workshop-slides.pptx - slides used during the debiasing workshop.<br>7. Debiasing-workshop-plan.docx - a detailed plan of the debiasing workshop, explaining how exactly the debiasing presentation should be used.<br>8. Consent_form.docx - a consent form that was signed by the participants from one of the author's countries, where guidelines suggested the use of gathering additional written consent.<br><br></p>
Data and models for "An image-computable model of speeded decision-making"
<p>Lost in Migration gameplay data and trained models for:</p> <div>Jaffe, P. I., Gustavo, X. S. R., Schafer, R. J., Bissett, P. G., Poldrack, R. A. An image-computable model of speeded decision-making. <em>eLife</em> <strong>13</strong>, RP98351 (2024).</div> <div> </div> <p>This dataset can be used to reproduce all of the results of the manuscript, following the instructions in the code repository for the paper: <a href="https://github.com/pauljaffe/vam">https://github.com/pauljaffe/vam</a>.</p> <p>The dataset includes the following components:</p> <p><strong>gameplay_data.zip:</strong> Trial-level gameplay metadata for Lost in Migration. Lost in Migration is a variant of the flanker task offered as a part of the Lumosity cognitive training platform (Lumos Labs, Inc.). The .zip file includes a separate .csv file for each of the 75 Lumosity users (participants) that we trained models on. Each .csv file has one row per trial with the following fields/columns: "anon_id", numerical identifier for the Lumosity user; "nth_play", the nth gameplay of Lost in Migration for this user; "trial", the nth trial for the current gameplay; "xpos", the signed horizontal distance from the center of the target bird to the left edge of the game window (pixels, non-negative); "ypos", the signed vertical distance from the center of the target bird to the bottom edge of the game window (pixels, non-negative); "flanker_direction", (L/R/U/D); "response_direction", (L/R/U/D); "target_direction", (L/R/U/D); "response_time", (ms); "stimulus_layout", numerical code for the layout of the bird flock for the current trial (0: horizontal line, 1: vertical line, 2: cross, 3: <, 4: >, 5: v, 6: ^)<strong>.</strong></p> <p><strong>vam_models.zip:</strong> Parameters for the 75 visual accumulator models (VAMs) analyzed in the manuscript.</p> <p><strong>task_opt_models.zip:</strong> Parameters for the 75 task-optimized models analyzed in the manuscript.</p> <p><strong>metadata.csv:</strong> Metadata for each Lumosity user that a VAM/task-optimized model was trained on. The .csv file has one row per user with the following fields/columns: "user_id", numerical identifier for the Lumosity user (same as "anon_id" in gameplay_data.zip); "gender", self-reported gender ('m', 'f', or null, indicating no response was given); "binned_age", age bucketed into decade-long bins (20-29, 30-39... 80-89).</p> <p><strong>derivatives.zip:</strong> The RTs/choices generated by the trained models, organized into separate folders by model type (vam/task_opt/binned_rt) and user ID. Also includes a "summary_stats" folder with analysis products of the model activations and outputs.</p> <p><strong>graphics.zip:</strong> Image files used to create the visual stimuli from the gameplay metadata.</p> <p><strong>example_model_inputs.zip: </strong>The processed visual stimuli and gameplay data used as inputs to train one model (user ID 182). Note we provide instructions to recreate the stimuli and other model inputs for all models in the code repository.</p>
Making the most of your pollinators: An epiphytic fig tree encourages its pollinators to roam between figs
<p><i>Ficus</i> species are characterised by their unusual enclosed inflorescences (figs) and their relationship with obligate pollinator fig wasps (Agaonidae). Fig trees have a variety of growth forms, but true epiphytes are rare; one example is the <i>Ficus deltoidea</i> of South-east Asia. Presumably as an adaptation to epiphytism, inflorescence design in this species is exceptional, with very few flowers in female (seed-producing) figs and unusually large seeds. Figs on male (pollinator offspring-generating) trees have many more flowers. Many fig wasps pollinate one fig each, but because of the low number of flowers per fig, efficient utilization of <i>F. deltoidea</i>'s pollinators depends on pollinators entering several female figs. We hypothesised that it is in the interest of the plants to allow pollinators to re-emerge from figs on both male and female trees and that selection favours pollinator roaming because it increases their own reproductive success. Our manipulations of <i>Blastophaga</i> sp. pollinators in a Malaysian oil palm plantation confirmed that individual pollinators do routinely enter several figs of both sexes. Entering additional figs generated more seeds per pollinator on female trees and more offspring on male trees. Pollinator offspring sex ratios in subsequently-entered figs were often less female-biased than in the first figs they entered, which reduced their immediate value to male trees because only female offspring carry their pollen. Small numbers of large seeds in female figs of epiphytic <i>F. deltoidea</i> may reflect constraints on overall female fig size, because pollinator exploitation depends on mutual mimicry between male and female figs.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.