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119 results for “arguments”
TABLE 4 in Revisiting the description of Atractoscion macrolepis (Perciformes: Sciaenidae): Another argument for comprehensive taxonomy
<p><b>TABLE 4.</b> Standardised coefficients of the 16 transformed morphometric variables for the canonical root, and the eigenvalues and cumulative variance explained by this root, extracted from the Discriminant Functions Analysis separating <i>Atractoscion aequidens</i> and <i>A. macrolepis</i>.</p><table><tbody><tr><th>Variable</th><th>Standardised coefficient</th></tr></tbody><tbody><tr><th><i>TL’ BD’ 2DFL’ CPL’ MCL’ LCLL’ AFBL’ AFL’ PFB’ PFL’ PLFL’ HL’ OD’ SNL’ PSF’ GFL’</i></th><td>-0.287 0.177 -0.239 -0.252 0.612 -0.685 -0.353 0.302 0.470 0.603 -0.455 0.644 -0.367 -0.593 -0.361 0.495</td></tr><tr><th>Eigenvalue Cumulative proportion</th><td>7.337 100%</td></tr></tbody></table>
TABLE 6 in Revisiting the description of Atractoscion macrolepis (Perciformes: Sciaenidae): Another argument for comprehensive taxonomy
<p><b>TABLE 6.</b> Frequency distribution of the number of pectoral fins rays in <i>Atractoscion aequidens</i> and <i>A. macrolepis</i>.</p><table><tbody><tr><th>Species</th><th></th><th>Pectoral fin rays (P)</th><th></th><th></th><th></th><th>Lateral line scales (LL)</th><th></th><th></th></tr></tbody><tbody><tr><th></th><td>16</td><td>17</td><td>18</td><td>19</td><td>68</td><td>69</td><td>70</td><td>71</td><td>72</td><td>73</td><td>74</td><td>75</td></tr><tr><th><i>Atractoscion aequidens</i></th><td>2</td><td>18</td><td>13</td><td></td><td></td><td></td><td></td><td>2</td><td>4</td><td>4</td><td>4</td><td>1</td></tr><tr><th><i>Atractoscion macrolepis</i></th><td>1</td><td>21</td><td>27</td><td>3</td><td>1</td><td></td><td>8</td><td>5</td><td>7</td><td>5</td><td>5</td><td></td></tr></tbody></table>
TABLE 9 in Revisiting the description of Atractoscion macrolepis (Perciformes: Sciaenidae): Another argument for comprehensive taxonomy
<p><b>TABLE 9.</b> Standardised coefficients of the four (of eight) transformed otolith variables for the canonical root extracted from the Discriminant Functions Analysis separating <i>Atractoscion aequidens</i> and <i>A. macrolepis</i>. and the eigenvalue of this root and cumulative percentage of the variance explained.</p><table><tbody><tr><th>Variable</th><th>Canonical root</th></tr></tbody><tbody><tr><th><i>OH’ OCD’ TOBC’ TOTC’</i></th><td>-2.026 0.498 -1.407 2.843</td></tr><tr><th>Eigenvalue Cumulative proportion</th><td>1.457 100%</td></tr></tbody></table>
TABLE 3. A in Revisiting the description of Atractoscion macrolepis (Perciformes: Sciaenidae): Another argument for comprehensive taxonomy
<p><b>TABLE 3.</b> <i>A posteriori</i> reclassification and percentage classification success of individuals to the two respective species, <i>Atractoscion aequidens</i> and <i>A. macrolepis</i>, based on classification functions derived in the Discriminant Functions Analyses of 16 transformed morphometric variables, two meristic counts and four otolith variables, respectively.</p><table><tbody><tr><th>Data set</th><th>Species</th><th>Percent correct</th><th><i>A posteriori</i> classification</th></tr></tbody><tbody><tr><th></th><td></td><td></td><td><i>A. aequidens</i></td><td><i>A. macrolepis</i></td></tr><tr><th>Morphometric</th><td><i>A. aequidens</i> <i>A. macrolepis</i> Total</td><td>100.00 98.03 98.73</td><td>28 1</td><td>0 50</td></tr><tr><th>Meristic</th><td><i>A. aequidens</i> <i>A. macrolepis</i> Total</td><td>80.00 74.19 76.09</td><td>12 8</td><td>3 23</td></tr><tr><th>Otoliths</th><td><i>A. aequidens</i> <i>A. macrolepis</i> Total</td><td>87.50 81.48 83.72</td><td>14 5</td><td>2 22</td></tr></tbody></table>
Figure 2 from: Heger T, Zarrieß S, Algergawy A, Jeschke JM, König-Ries B (2022) INAS: Interactive Argumentation Support for the Scientific Domain of Invasion Biology. Research Ideas and Outcomes 8: e80457. https://doi.org/10.3897/rio.8.e80457
Figure 2 The main architecture of INAS.
"AI as an Ally?" : AI mediation tools to support undergraduates' argumentative skills
<p>Argumentative skills are indispensable both personally and professionally to process complex information (CoI) relating to the critical reconstruction of meaning through critical thinking (CT). This remains a particularly relevant priority, especially in the age of social media and artificial intelligence-mediated information. Recently, the public dissemination of what has been called generative artificial intelligence (GenAI), with the particular example of ChatGPT (OpenAI, 2022), has made it even easier today to access and disseminate information, written or not, true or not. New tools are needed to critically address post-digital information abundance. </p> <p>In this context, argumentative maps (AMs), which are already used to develop argumentative skills and critical thinking, are studied for multimodal and dynamic information visualization, comprehension, and reprocessing. In this regard, the entry of generative AI into university classrooms proposes a novel scenario of multimodality and technological dynamism. </p> <p>Building on the Vygotskian idea of mediation and the theory of "dual stimulation" as applied to the use of learning technologies, the idea was to complement AMs with the introduction of a second set of stimuli that would support and enhance individual activity: AI-mediated tools. With AMs, an attempt has been made to create a space for understanding, fixing, and reconstructing information, which is important for the development of argumentative skills. On the other hand, by arranging forms of critical and functional interaction with ChatGPT as an ally in understanding, reformulating, and rethinking one's argumentative perspectives, a new and comprehensive argumentative learning process has been arranged, while also cultivating a deeper understanding of the artificial agents themselves. </p> <p>Our study was based on a two-group quasi-experiment with 27 students of the “Research Methods in Education” course, to explore the role of AMs in fixing and supporting multimodal information reprocessing. In addition, by predicting the use of the intelligent chatbot ChatGPT, one of the most widely used GenAI technologies, we investigated the evolution of students' perceptions of its potential role as a “study companion” in information comprehension and reprocessing activities with a path to build a good prompt. </p> <p>Preliminary analyses showed that in both groups, AMs supported the increase in mean CoI and CT levels for analog and digital information. However, the group with analog texts showed more complete reprocessing.<br>The interaction with the chatbot was analyzed quantitatively and qualitatively, and there emerged an initial positive reflection on the potential of ChatGPT and increased confidence in interacting with intelligent agents after learning the rules for constructing good prompts.</p> <p> </p> <p>This Zenodo record follows the full analysis process with R (https://cran.r-project.org/bin/windows/base/ ) and Nvivo (https://lumivero.com/products/nvivo/) composed of the following datasets, script and results:</p> <p>1. Comprehension of Text and AMs Results - Arg_G1.xlsx & Arg_G2.xlsx</p> <p>2. Opinion and Critical Thinking level - Opi_G1.xlsx & Opi_G2.xlsx</p> <p>3. Data for Correlation and Regression - CorRegr_G1.xlsx & CorRegr_G2.xlsx</p> <p>4. Interaction with ChatGPT - GPT_G1.xlsx & GPT_G2.xlsx</p> <p>5. Descriptive and Inferential Statistics Comprehension and AMs Building - Analysis_RES_Comprehension.R</p> <p>6. Descriptive and Inferential Statistics Opinion and Critical Thinking level - Analysis_RES_Opinion.R</p> <p>7. Correlation and Regression - Analysis_RES_CorRegr.R</p> <p>8. Descriptive and Inferential Statistics Interaction with ChatGPT - Analysis_RES_ChatGPT.R</p> <p>9. Sentiment Analysis - Sentiment Analysis_G1.R & Sentiment Analysis_G2.R</p> <p>10. Vocabulary Frequent words - Vocabulary.csv</p> <p>11. Codebook qualitative Analysis with Nvivo (Codebook.xlsx)</p> <p>12. Results Nvivo Analysis G1 - Codebook - ChatGPT2 G1.docx</p> <p>13. Results Nvivo Analysis G2 - Codebook - ChatGPT2 G2.docx</p> <p> </p> <p>Any comments or improvements are welcome!</p>
Touché23-Argument-Retrieval-for-Controversial-Questions
<p>Data for the <a href="https://touche.webis.de/clef23/touche23-web/argument-retrieval-for-controversial-questions.html">Argument Retrieval for Controversial Questions task</a> at Touché 2023.</p>
Testing the Persuasiveness of Pro-inoculation Arguments
ClinicalTrials.gov study NCT05804825. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Impact of a Brief Motivational Intervention Including Counter-marketing Arguments With a Population of Patients With Moderate to Severe Alcohol Use Disorders Who Are Followed up on an Outpatient Basis
ClinicalTrials.gov study NCT04881500. IPD Sharing: YES. Countries: 1. Publications: 0.
Strumenti valutativi per abilità argomentative e pensiero critico [Assessment tools for argumentative skills and critical thinking]
<p>[Abstract del lavoro di tesi di dottorato] La realtà quotidiana è segnata da innumerevoli momenti di incontro e discussione con l'altro. Il contesto dei nuovi media, delle intelligenze artificiali (IA) e nello specifico di quella che è stata definita IA generativa (GenAI), con l'esempio particolare di ChatGPT, sta cambiando il modo in cui le persone comunicano tra loro, cercano informazioni e partecipano a discussioni. Oggi essere in grado di comprendere e formulare una prospettiva davvero critica sulle informazioni e sulla tecnologia che le media è fondamentale ma ancora più complesso. <br>In questo contesto, partendo dall’idea vygotskiana di mediazione e di “doppia stimolazione” applicata all’uso di tecnologie per l’apprendimento, da una parte si è indagato come le mappe argomentative (MA), una modalità di visualizzazione del ragionamento alla base di un’argomentazione, potessero svilupparsi per supportare gli studenti universitari nella navigazione, comprensione e ricostruzione di informazioni sempre più multimodali e dinamiche; e dall’altra, predisponendo forme di interazione critica con l’agente artificiale ChatGPT, per la riformulazione e ripensamento delle proprie prospettive argomentative, si è avviato un più ampio processo di apprendimento argomentativo e di comprensione profonda degli agenti artificiali.<br>Il lavoro di ricerca è partito da un primo momento esplorativo di analisi della letteratura disponibile per definire il contesto entro cui ci muoviamo, in termini di emergenti esigenze educative e argomentative e di panorama di ricerca in merito, arrivando successivamente a snodarsi in indagini sempre più mirate al contesto nazionale e ad approfondire la riflessione sull’apprendimento degli studenti universitari nei contesti dei nuovi media. Il focus si è poi arricchito con una seconda fase quantitativa quasi-sperimentale e una serie di percorsi didattici che hanno evidenziato come le competenze argomentative siano migliorabili anche e specialmente in contesti sempre più mediati da IA. Tuttavia, queste previsioni entusiaste hanno dovuto commisurarsi alla luce dei risultati della ricerca empirica stessa e delle persistenti incertezze riguardo a come gli studenti si possano relazionare con le forme del post-digitale, inclusi gli agenti artificiali.</p> <p>In questo record Zenodo si condividono gli strumenti valutativi applicati e testati per le fasi di lavoro: </p> <p>1) Abilità Argomentative: Check-list elementi argomentativi.docx</p> <p>2) Opinione e capacità argomentativa: Griglia Argomentazione Opinione.docx</p> <p>3) Impatto dell'interazione con un chatbot: Rubrica Analisi Interazione chatbot.docx</p> <p>4) Pensiero critico applicato al compito di comprensione ed elaborazione argomentativa: Rubrica CT prima versione.docx e Rubrica CT ultima versione.docx</p> <p> </p> <p>Policy: accesso ristretto fino alla pubblicazione della tesi di dottorato, da cui hanno origine gli strumenti.</p> <p>Commenti e suggerimenti sono i benvenuti!</p>
Reading practices and textual production in interfaces in the school setting: promoting argumentative consistency for Writing/ENEM
Open the record for dataset details and reuse information.
Maps and AI: a lab to support undergraduates' argumentative skills
<p>Argumentative abilities are required both individually and professionally to digest complex information (CoI) related to the critical reconstruction of meaning (critical thinking - CT). This is still a particularly important objective, especially in the age of social media and artificial intelligence-mediated information. Recently, the advent of the generative artificial intelligence (GenAI), with the specific example of ChatGPT (OpenAI, 2022), has made it easier to obtain and share knowledge, and new tools are sorely needed to deal with the abundance of post-digital information without getting lost.</p> <p>The research activity aimed to investigate the extent to which AMs, already used to develop argumentative skills and critical thinking, helped students increase their level of comprehension of multimodal information and their ability to rework their own opinion about it. Juxtaposed to this line of thinking was the idea of adopting the ChatGPT intelligent chatbot to extrapolate and analyze whether an initial communicative and argumentative interaction with an intelligent agent would help students reframe their arguments. </p> <p>Our study was based on a two-group quasi-experiment with 24 students in the Education Evaluation teaching, from the Bachelor of Science in Education and Training, Education and Human Resource Development curriculum, University of Padua (L-19). The control group (G1) conducted the information comprehension and reprocessing course in an analogue context, and the experimental group (G2) was exposed to multimodality with texts and tutorials bearing dynamic and digital elements. Both groups then interacted with ChatGPT, integrated within an interactive lecture in the middle of the course. </p> <p>Preliminary analyses showed that in both groups, AMs improved students' text comprehension and critical thinking. The group that used analog texts (G1) showed more uniform improvement than the group with multimodal texts (G2), which had greater variability in results. However, multimodality stimulated deeper and more integrative reflection. The use of ChatGPT showed potential to improve information reprocessing, with students recognizing the usefulness of AI and taking a critical approach to its interaction. Future research will aim to better integrate these tools to maximize learning.</p> <p>This Zenodo record follows the full analysis process with R (https://cran.r-project.org/bin/windows/base/ ) composed of the following datasets and script:</p> <p>1. Comprehension of Text and AMs Results - Arg_G1.xlsx & Arg_G2.xlsx</p> <p>2. Opinion and Critical Thinking level - Opi_G1.xlsx & Opi_G2.xlsx</p> <p>3. Data for Correlation and Regression - CorRegr_G1.xlsx & CorRegr_G2.xlsx</p> <p>4. Descriptive and Inferential Statistics Comprehension and AMs Building - Comprehension&AM_VAL.R</p> <p>5. Descriptive and Inferential Statistics Opinion and Critical Thinking level - Opinion&CriticalThinking_VAL.R</p> <p>6. Correlation and Regression - Analysis_VAL.R</p> <p> </p> <p>Any comments or improvements are welcome!</p>
Argumentative Skills in Higher Education: An analysis of university course Syllabus.
<p>Argumentative skills are personally and professionally essential to digest complicated information (CoI) associated with the critical reconstruction of meaning (critical thinking - CT). This is a vital goal, especially in the age of social media and artificial intelligence-mediated information. Recently, the introduction of generative artificial intelligence (GenAI), particularly ChatGPT (OpenAI, 2022), has made it much simpler to collect and exchange knowledge. New tools are desperately needed to deal with the glut of post-digital information without becoming lost.</p> <p>After exploring the landscape of argumentative skills and techniques for their development, an investigation of practices in use in Italian universities was undertaken. The analysis used university syllabi, which are considered key educational tools, to provide a comprehensive overview of the course to be undertaken. The syllabi contain key information such as objectives, competencies, assignments and assessment strategies. </p> <p>The research examined education science courses to understand the importance of argumentative skills, using stratified random sampling to proportionally represent all public universities with education science departments. 133 syllabi were selected through web scraping and web crawling techniques using R software (https://cran.r-project.org/bin/windows/base/), with manual addition to overcome technical limitations. </p> <p>The analysis included text mining techniques to identify documents containing keywords related to argumentative skills. These documents were then subjected to quantitative and qualitative content analysis. Biggs' (2003) "Constructive Alignment" principles were used to assess the alignment of goals, activities, and assessments in the syllabi. Categories of analysis included the detection of argumentative skills, their alignment, and connection to the course, with a focus on presence, level of treatment, and consistency of alignment.</p> <p>This Zenodo record follows the full analysis process with R and Nvivo (https://lumivero.com/products/nvivo/) composed of the following datasets, script and results:</p> <p>1. List of Universities with URLs - Elenco Università.xlsx</p> <p>2. Web Scraping Script - WebScarping.R</p> <p>3. Text Mining Script - TextMining.R</p> <p>4. List of the most frequent words - Vocabulary.csv</p> <p>5. Sentiment Analysis of the corpus - Sentiment Analysis.R</p> <p>6. List of documents from sorting by Keywords - frasi_chiave.docx</p> <p>7. Codebook qualitative Analysis with Nvivo - Codebook.xlsx</p> <p>8. Results Nvivo Analysis Syllabi - Codebook-Syllabi.docx</p> <p> </p> <p>Any comments or improvements are welcome!</p>
Argumentative Skills in Higher Education: a national survey between italian undergraduates
<p>A survey was conducted on the argumentative skills of Italian university students through a survey. The survey aimed to assess text comprehension and the ability to reframe personal thought, and used a structured questionnaire to collect data from a representative sample of students from various Italian universities. The survey was organized on a Google Sites platform and included three thematic tracks: "Ethics and Research," "Us and AIs," and "Technology and Childhood." </p> <p>Results showed that students had difficulty recognizing elements such as "problem" and "thesis" but fared better with supporting arguments and counter-arguments. The average scores were moderate for both text comprehension (3.64 out of 7) and critical thinking (5.58 out of 10). </p> <p>The analysis found that the ability to use factual evidence is critical to overall performance. This suggests the need to improve students' training in analysis and argumentation skills by promoting continued practice of argumentative and critical thinking exercises for greater academic and professional success.</p> <p>This Zenodo record follows the full analysis process with R (https://cran.r-project.org/bin/windows/base/) and Nvivo (https://lumivero.com/products/nvivo/) composed of the following datasets, script and results:</p> <p>1. Comprehension of Text and Opinion - Arg_Skills.xlsx</p> <p>2. Descriptive and Inferential Statistics Argumentative Skills - Analysis_Arg_Skills.R</p> <p> </p> <p>Any comments or improvements are welcome!</p>
TABLE 8 in Revisiting the description of Atractoscion macrolepis (Perciformes: Sciaenidae): Another argument for comprehensive taxonomy
<p><b>TABLE 8.</b> Factor loadings, eigenvalues and the variation explained by the first three (of eight) principal components extracted in the Principal Components Analysis of eight transformed otolith variables examining variation among <i>Atractoscion aequidens</i> and <i>A. macrolepis</i>.</p><table><tbody><tr><th>Variable</th><th>Principal Components</th><th></th><th></th></tr></tbody><tbody><tr><th><i>OH’ OL’ OCD’ OrCD’ TOTC’ TOBC’ WC’ OW’</i></th><td>PC1 0.929 0.966 0.573 0.893 0.902 0.976 0.648 0.084</td><td>PC2 -0.068 -0.050 0.136 0.072 -0.071 -0.042 -0.015 0.985</td><td>PC3 -0.235 0.053 0.743 0.071 -0.117 0.036 -0.372 -0.134</td></tr><tr><th>Eigenvalue % Variation Cumulative variation</th><td>5.116 63.95 63.95</td><td>1.007 12.59 76.54</td><td>0.787 9.83 86.37</td></tr></tbody></table>
Aspiration Risk Assessment by Gastric Ultrasound in eMErgency Surgery and ANesThetic Decision-making: The ARGUMENT Study
ClinicalTrials.gov study NCT06303492. IPD Sharing: NO. Countries: 0. Publications: 0.
Argument Graphs Extracted From the Online Debate Portal Kialo
<p>For our paper "Towards an Argument Mining Pipeline Transforming Texts to Argument Graphs" published at COMMA 2020, we extracted debates from the online debate portal Kialo. We used it to train multiple classifiers and evaluate our pipeline, but the data may be useful in other areas as well. The argument graphs are saved in a format similar to the one used by OVA (<a href="https://ova.arg-tech.org">https://ova.arg-tech.org</a>). They can be parsed using our Python library (<a href="https://pypi.org/project/recap-argument-graph/">https://pypi.org/project/recap-argument-graph/</a>).</p> <p>If you use the data, please cite our work as follows:</p> <pre><code>@inproceedings{Lenz2020ArgumentMiningPipeline, address = {{Perugia, Italy}}, archiveprefix = {arXiv}, author = {Lenz, Mirko and Sahitaj, Premtim and Kallenberg, Sean and Coors, Christopher and Dumani, Lorik and Schenkel, Ralf and Bergmann, Ralph}, booktitle = {Proceedings of the 8th {{International Conference}} on {{Computational Models}} of {{Argument}}}, doi = {10.3233/FAIA200510}, editor = {Prakken, Henry and Bistarelli, Stefano and Santini, Francesco and Taticchi, Carlo}, eprint = {2006.04562}, eprinttype = {arxiv}, pages = {263--270}, publisher = {{IOS Press}}, series = {Frontiers in {{Artificial Intelligence}} and {{Applications}}}, title = {Towards an {{Argument Mining Pipeline Transforming Texts}} to {{Argument Graphs}}}, volume = {326}, year = {2020}, Bdsk-Url-1 = {https://doi.org/10.3233/FAIA200510}} </code></pre> <p> </p>
DATASET - In search of an effective source use pattern for writing argumentative and informative synthesis texts
<p>This SPSS file contains the data corresponding to the study as published in the article <em>In search of an effective source use pattern for writing argumentative and informative synthesis texts </em>(Vandermeulen, Van den Broek, Van Steendam, & Rijlaarsdam, 2019). </p> <p> </p>
Le mappe argomentative come comparator di feedback interno: un laboratorio per studenti universitari [Argument maps as comparator for internal feedback: a Lab for undergraduate students ]
<p>Il feedback interno è un costrutto divenuto recentemente rilevante per il suo impatto sulla regolazione metacognitiva e affettivo-relazionale, in relazione a diverse skills e contesti formativi. In particolare, il concetto di comparator, ovvero gli strumenti, gli interventi o le risorse che attivano il feedback interno, necessita di approfondimento tramite ricerca empirica. In questo contributo si parte dall’ipotesi che la componente visiva delle mappe argomentative (MA), già legate allo sviluppo di skills argomentative e di pensiero critico, possa essere una fonte generativa di comparazione concreta, permettendo una comprensione facilitata ed una migliore ricostruzione del senso dell’informazione testuale argomentativa. Infatti, le AM diagrammano le relazioni logiche fra i diversi enunciati, permettendo di seguire e di comprendere meglio la catena di ragionamento. Per verificare la suddetta ipotesi, è stato condotto uno studio sperimentale per indagare in che misura un percorso con le AM favorisse gli studenti nell’incremento di: a) feedback interno (FI) associato quindi a b) il livello di competenza di comprensione del testo (CoT) e a c) lo sviluppo di pensiero critico (CT). </p> <p>Questo record Zenodo presenta i quattro dataset (anonimizzati) raccolti durante la sperimentazione ed adottati per le analisi descrittive ed inferenziali, di cui si presenta una parte nel presente paper. Inoltre, si allega ilo Script R utile alla replica delle analisi. Qualsiasi commento o miglioramento è benvenuto!</p> <p>=========</p> <p>ENGLISH</p> <p>Internal feedback is a construct that become recently relevant for the impact it has on metacognitive and affective-relational regulation, in relation to different skills and learning contexts. In particular, the concept of comparator, i.e. the tools, interventions, or resources that activate internal feedback, requires the support of empirical research. In this contribution, we take as an initial hypothesis that the argument maps’ (AM) visual component, already linked to the development of argumentative and critical thinking skills, could be a generative source of concrete comparison, allowing for a facilitated comprehension and an improvement of the sense-making abilities within argumentative texts. In fact, AMs diagram the logical relationships between different utterances, allowing the learner to keep track and better understand the reasoning chain. To test the above hypothesis, an experimental study was conducted to investigate the extent to which a course with AMs favored students in increasing: a) internal feedback (IF), associated with b) their level of text comprehension (CoT) and hence, c) critical thinking (CT). </p> <p>This Zenodo record presents the four (anonymised) datasets collected during the experimentation and adopted for the descriptive and inferential analyses, part of which are presented in this paper. Also attached is the R Script useful for replicating the analyses. Any comments or improvements are welcome!</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.