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68 results for “dialogue”
TDA4ContextualEmbeddings - Public - Debug Data for the codebase of the publication "Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction"
<p>Debug dataset for testing the <a href="https://gitlab.cs.uni-duesseldorf.de/general/dsml/tda4contextualembeddings-public">codebase</a> of the paper <a href="https://doi.org/10.18653/v1/2024.sigdial-1.31">“Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction”</a> published at the 25th Meeting of the Special Interest Group on Discourse and Dialogue, Kyoto, Japan (SIGDIAL 2024).</p>
Fon French Daily Dialogues Parallel Data
<p>We aim to collect, clean, and store corpora of Fon and French sentences for Natural Language Processing researches including Neural Machine Translation, Named Entity Recognition, etc. for Fon, a very low-resourced and endangered African native language.</p> <p>Fon (also called Fongbe) is an African-indigenous language spoken mostly in Benin, Togo, and Nigeria - by about 2 million people.</p> <p>As training data is crucial to the high performance of a machine learning model, the aim of this project is to compile the largest set of training corpora for the research and design of translation and NLP models involving Fon.</p> <p>Through crowdsourcing, Google Form Surveys, we gathered and cleaned #25377 parallel Fon-French# all based on daily conversations.</p> <p>To the crowdsourcing, creation, and cleaning of this version have contributed:</p> <p>1) Name: Bonaventure DOSSOU<br> Affiliation: MSc Student in Data Engineering, Jacobs University<br> Contact: femipancrace.dossou@gmail.com</p> <p>2) Name: Ricardo AHOUNVLAME<br> Affiliation: Student in Linguistics<br> Contact: tontonjars@gmail.com</p> <p>3) Name: Fabroni YOCLOUNON<br> Affiliation: Creator of the Label IamYourClounon<br> Contact: iamyourclounon@gmail.com</p> <p>4) Name: BeninLangues<br> Affiliation: BeninLangues<br> Contact: https://beninlangues.com/</p> <p>5) Name: Chris Emezue<br> Affiliation: MSc Student in Mathematics in Data Science, Technical University of Munich<br> Contact: chris.emezue@gmail.com</p> <p>_______________________________________________________</p> <p>To join as a contributor, please contact us at:<br> 1) https://twitter.com/bonadossou<br> 2) https://twitter.com/ChrisEmezue<br> 3) https://twitter.com/edAIOfficial<br> Or contact Bonaventure Dossou (femipancrace.dossou@gmail.com), Chris Emezue (chris.emezue@gmail.com)<br> _______________________________________________________</p> <p>Clavier Fongbé (WebView): https://bonaventuredossou.github.io/clavierfongbe/ (Made by Bonaventure Dossou)<br> Clavier Fongbé (Mobile Android Version): https://play.google.com/store/apps/details?id=com.fulbertodev.clavierfongbe&hl=en&gl=US (Fabroni Yoclounon, Bonventure Dossou et. al.)</p>
ACTIV-ES: a comparable Spanish corpus comprised of film dialogue from Argentine, Mexican and Spanish productions
<p><strong>DESCRIPTION</strong>: ACTIV-ES is a comparable Spanish corpus comprised of film dialogue from Argentine, Mexican and Spanish productions. Titles for each of these three countries were seeded from the Internet Movie Database, subtitle data for the hearing impaired was provided by Opensubtitles.org and was post-processed to correct/remove subtitle, OCR and diacritic artifacts and annotated for part-of-speech.</p> <p>The data is available in two main formats: 1) running text for each document and 2) 1:5 gram aggregate files. Each format includes a plain text and part-of-speech annotated version. Document names reflect the language code, country, year, title, type, genre (first genre listed in the IMDb), and IMDb ID.</p> <p>For more information about the development and evaluation of these resources and to cite this work refer to:</p> <p>Francom, J., Hulden, M. and Ussishkin, A.. (2014) ACTIV-ES: a comparable, cross-dialect corpus of 'everyday' Spanish from Argentina, Mexico, and Spain. In Proceedings of the Ninth Annual Language Resources and Evaluation Conference, Reykjavik, Iceland. European Language Resources Association (ELRA).</p> <p>In <strong>version .02</strong> of the tagged running format corpus in the /eagles directory has been added which includes the EAGLES tagset. This tagset is much more fleshed out than the simplified tagset in the /tagged directory. For information on the tagset refer here: <a href="http://nlp.lsi.upc.edu/freeling/doc/tagsets/tagset-es.html">http://nlp.lsi.upc.edu/freeling/doc/tagsets/tagset-es.html</a>.</p>
Data related to: Hippocampal ripples and their coordinated dialogue with the default mode network during recent and remote recollection, Norman et al. (2021)
<p>This dataset contains intra-cranial EEG recordings and analysis code related to the paper: "Hippocampal ripples and their coordinated dialogue with the default mode network during recent and remote recollection" by Norman et al. (https://doi.org/10.1016/j.neuron.2021.06.020)<br> The study investigates the role of hippocampal ripples in the human brain during retrieval of recent and remote autobiographical memories and semantic facts. The intracranial recordings underwent standard preprocessing as described in the paper and were stored in EEGLAB datasets. The analysis code that accompanies the dataset implements the main analyses described in the paper.</p> <p>The dataset includes the following zip files:</p> <ul> <li>iEEG data and main analysis code: <ul> <li>“Norman_et_al_2021_iEEG_data_and_code_1.zip" </li> <li>“Norman_et_al_2021_iEEG_data_and_code_2.zip" </li> </ul> </li> <li>Patients' anatomical data: <ul> <li>“Norman_et_al_2021_Freesurfer.zip”</li> </ul> </li> <li>Additional toolboxes (developed by others): <ul> <li>“MATLAB_toolboxes.zip”</li> </ul> </li> </ul> <p>The code is written primarily in Matlab (version R2018b) and runs on a desktop computer with a 3.4Ghz Intel Core i7-6700 CPU with 64GB RAM. Matlab's Signal Processing Toolbox is required, as well as EEGLAB, Unfold toolbox, and several other open-source toolboxes.</p>
Can digital platforms be regulated? A Dialogue Between The European Union and México
<p>Can digital platforms be regulated? A Dialogue Between The European Union and México</p> <p>This video presentation was produced as a Digital Knowledge Product for the Horizon 2020 MSCA RISE project, Promoting Research on Digitalisation in Emerging Powers and Europe towards Sustainable Development (PRODIGEES, GA #873119).</p> <p>The original publishing agency is Instituto Mora EN VIVO 1. The original publishing platform is YouTube. The link: https://www.youtube.com/watch?v=VY5A8GdIjy4</p>
Supplementary material 1: Reviewer Comments from: Widening the circle of care: An arts-based, participatory dialogue with stakeholders on cancer care for First Nations, Inuit, and Métis peoples in Ontario, Canada - Research Ideas and Outcomes 2: e9115 (25 May 2016) https://doi.org/10.3897/rio.2.e9115
The attached file includes the evaluation of the postdoctoral fellowship application from three reviewers. Guidelines for reviewers are available online for more inforamtion (http://www.cihr-irsc.gc.ca/e/33043.html), including the rating scale that is used to score each section of the evaluation.
EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems
<p>This is the dataset created for the paper, "EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion Recognition in Task-Oriented Dialogue Systems" (https://arxiv.org/abs/2109.04919).</p> <p>EmoWOZ is based on MultiWOZ, a multi-domain task-oriented dialogue dataset (https://github.com/budzianowski/multiwoz). It contains more than 11K task-oriented dialogues with more than 83K emotion annotations of user utterances. In addition to Wizard-of-Oz dialogues from MultiWOZ, we collect human-machine dialogues within the same set of domains to sufficiently cover the space of various emotions that can happen during the lifetime of a data-driven dialogue system. There are 7 emotion labels, which are adapted from the OCC emotion models.</p> <p>For data format and label definition, please refer to README.md. </p>
ARL Human-Robot Dialogue (HuRDL) Corpus
<p>ARL HuRDL (Human Robot Dialogue Learning) is a labeled corpus of 22 human-human dialogues in a situated reference resolution task. </p>
Durably reducing conspiracy beliefs through dialogues with AI
Open the record for dataset details and reuse information.
Human annotation data for ACL 2020 paper "Designing Precise and Robust Dialogue Evaluators"
<p>Human annotation data for ACL 2020 paper "Designing Precise and Robust Dialogue Evaluators"</p>
Data from: Cortical reactivation of spatial and non-spatial features coordinates with hippocampus to form a memory dialogue
<p>Episodic memories comprise diverse attributes of experience distributed across neocortical areas. The hippocampus is integral to rapidly binding these diffuse representations, as they occur, to be later reinstated. However, the nature of the information exchanged during this hippocampal-cortical dialogue remains poorly understood. A recent study has shown that the secondary motor cortex carries two types of representations: place cell-like activity, which were impaired by hippocampal lesions, and responses tied to visuo-tactile cues, which became more pronounced following hippocampal lesions. Using two-photon Ca<sup>2+</sup> imaging to record neuronal activities in the secondary motor cortex of male Thy1-GCaMP6s mice, we assessed the cortical retrieval of spatial and non-spatial attributes from previous explorations in a virtual environment. We show that, following navigation, spontaneous resting state reactivations convey varying degrees of spatial (trajectory sequences) and non-spatial (visuo-tactile attributes) information, while reactivations of non-spatial attributes tend to precede reactivations of spatial representations surrounding hippocampal sharp-wave ripples.</p>
PxCorpus : A Spoken Drug Prescription Dataset in French for Spoken Language Understanding and Dialogue
<h2><strong>PxCorpus : A Spoken Drug Prescription Dataset in French</strong></h2><p> </p><p>PxCorpus is to the best of our knowledge, the first spoken medical drug prescriptions corpus to be distributed.</p><p>It contains 4 hours of transcribed and annotated dialogues of drug prescriptions in French acquired through an experiment with 55 participants experts and non-experts in drug prescriptions.</p><p> </p><p>The automatic transcriptions were verified by human effort and aligned with semantic labels to allow training of NLP models. The data acquisition protocol was reviewed by medical experts and permit free distribution without breach of privacy and regulation.</p><p> </p><h3>Overview of the Corpus</h3><p>The experiment has been performed in wild conditions with naive participants and medical experts.</p><p>In total, the dataset includes 2067 recordings of 55 participants (38% non-experts, 25% doctors, 36% medical practitioners), manually transcribed and semantically annotated.</p><p> </p><p>| Category | Sessions | Recordings | Time(m)|</p><p>|-----------------------| ------------- | --------------- | ----------- |</p><p>| Medical experts | 258 | 434 | 94.83 |</p><p>| Doctors | 230 | 570 | 105.21 |</p><p>| Non experts | 415 | 977 | 62.13 |</p><p>| Total | 903 | 1981 | 262.27 |</p><p> </p><h3>License</h3><p>We hope that that the community will be able to benefit from the dataset which is distributed with an attribution 4.0 International (CC BY 4.0) Creative Commons licence.</p><p> </p><h3>How to cite this corpus</h3><p> </p><p>If you use the corpus or need more details please refer to the following paper: A spoken drug prescription datset in French for spoken Language Understanding</p><p> </p><p>@InProceedings{Kocabiyikoglu2022,</p><p> author = "Alican Kocabiyikoglu and Fran{\c c}ois Portet and Prudence Gibert and Hervé Blanchon and Jean-Marc Babouchkine and Gaëtan Gavazzi",</p><p> title = "A spoken drug prescription datset in French for spoken Language Understanding",</p><p> booktitle = "13th Language Ressources and Evaluation Conference (LREC 2022)",</p><p> year = "2022",</p><p> location = "Marseille, France"</p><p>}</p><p>a more complete description of the corpus acquisition is available on arxiv </p><p>@misc{kocabiyikoglu2023spoken,</p><p> title={Spoken Dialogue System for Medical Prescription Acquisition on Smartphone: Development, Corpus and Evaluation},</p><p> author={Ali Can Kocabiyikoglu and François Portet and Jean-Marc Babouchkine and Prudence Gibert and Hervé Blanchon and Gaëtan Gavazzi},</p><p> year={2023},</p><p> eprint={2311.03510},</p><p> archivePrefix={arXiv},</p><p> primaryClass={cs.CL}</p><p>}</p><p> </p><h3>Project Structure</h3><p> </p><p>The project contains the following elements</p><p>.</p><p>├── LICENSE</p><p>├── PxDialogue/</p><p>├── PxSLU/</p><p>├── readme.md</p><p> </p><h3>PxSLU : Prescription Corpus for Spoken Language Understanding</h3><p> </p><h4>Directory Structure</h4><p>.</p><p>├── LICENSE</p><p>├── metadata.txt</p><p>├── paths.txt</p><p>├── PxSLU_conll.txt</p><p>├── readme.md</p><p>├── recordings</p><p>├── seq.in</p><p>├── seq.label</p><p>├── seq.out</p><p>├── Demo.ipynb</p><p>└── verifications.py</p><p> </p><h4>Recordings</h4><p> </p><p>The recordings directory contains the 903 recording sessions. Each session can contain several recordings. For instance,</p><p>the directory</p><p> recordings/J7aVvWb67L</p><p>contains the records </p><p> recording_0.wav recording_2.wav</p><p>which represent two attempts to record a drug prescription</p><p> </p><p>All records are stored as mono channel wav files of 16kHz 16bits signed PCM</p><p> </p><h4>Paths</h4><p> </p><p>contains the list of all the .wav files in the recordings directory</p><p>00MYcyVK0t/recording_0.wav</p><p>00MYcyVK0t/recording_2.wav</p><p>02Qp6ICj9Q/recording_0.wav</p><p>02Qp6ICj9Q/recording_1.wav</p><p>...</p><p> </p><p>All other files (metadata.txt, seq.*) refer to this list to describe the recording.</p><p> </p><h4>Metadata</h4><p> </p><p>contains the information about the participants:</p><p> </p><p>48,60+,F,non-expert</p><p>48,60+,F,non-expert</p><p>24,18–28,F,doctor</p><p>24,18–28,F,doctor</p><p>...</p><p> </p><p>The first column is the participant unique id, the second is the age range, the third is the gender and the final is the category of the participant in {doctor,expert, non-expert}. doctor correspond to a physician, (other)expert to a pharmacist or a biologist specialized in drugs while non-expert are other people not entering in these categories. The lines are synchronised with the paths.txt lines.</p><p> </p><h4>Labels</h4><p> </p><p>the three files seq.label, seq.in, seq.out represent respectivly the intent, the transcript and the entities in BIO format.</p><p> </p><p> seq.label | seq.in | seq.out</p><p>medical_prescription | flagyl 500 milligrammes euh qu/ en... | B-drug B-d_dos_val B-d_dos_up O O ...</p><p>medical_prescription | 3 comprimés par jour matin midi ... | B-dos_val B-dos_uf O O B-rhythm_tdte B-rhythm_tdte O B-rhythm_tdte ...</p><p> ... | ... | ...</p><p> </p><p>These lines are synchronised with the paths.txt lines.</p><p> </p><p>Another file "PxSLU_conll.txt" is provided in a format inspired by the conll format (https://universaldependencies.org/format.html). However, this one is *not* aligned with the acoustic records file paths.txt.</p><p> </p><h4>Scripts</h4><p> </p><p>verifications.py performs the checking of the alignement of all the seq.* paths.txt and metadata files. A user of the dataset does not need to use this script unless she plan to extend the datasets with her own data.</p><p>Demo.ipynb is a jupyter notebook that a user can run to search through the dataset. It is intended to let the user have a quicker and smoother view on the dataset.</p><p> </p><p> </p><h4>Data splits</h4><p>In the data_splits folder, you can find a data split of this dataset organized as following:</p><p>- train.txt: medical experts + non experts (80%) = 1128 samples</p><p>- dev.txt: medical experts + non experts (20%) = 283 samples</p><p>- test.txt: doctors (100%) = 570 samples</p><p> </p><p>Each file contains references to line numbers of the corpus. For example, first line of the test.txt is 904, seq.in file contains the utterance "nicopatch". Users can access the labels, slots, metadata using the same line number 904 in the parallel files (paths.txt,seq.out,seq.label,...).</p><p> </p><h3>PxDialogue : Prescription recording corpus for dialogue systems</h3><p> </p><p>PxDialogue corpus comes as an extension of the PxSLU corpus and provides additional information about the dialogues that was collected through spoken dialogue. This corpus includes two additional files:</p><p>├── events.txt</p><p>├── dialogue_annotations.txt</p><p> </p><p> </p><h4>Events.txt:</h4><p> </p><p>For each dialogue session, all dialogue events are given in this text file</p><p>which can be used to train/evaluate dialogue systems.</p><p> </p><p>Usage example:</p><p> </p><p>PxSLU (paths.txt)</p><p>- 00MYcyVK0t/recording_0.wav</p><p>- 00MYcyVK0t/recording_2.wav</p><p> </p><p>PxDialogue (events.txt)</p><p>- (-1, 'START', 'APP', None, 0) (1, 'user', 'ASR', 'flagyl 500 mg en cachet pendant 8 jours', 30) (1, 'system', 'TTS', 'Choisissez le médicament correspondant à votre recherche', 34) (2, 'user', 'UI', 'listview_item_clicked', 40) (2, 'system', 'TTS', 'Pourriez vous préciser la posologie pour le patient?', 40) (3, 'user', 'ASR', '3 comprimés par jour matin midi et soir pendant 10 jours', 64) (3, 'system', 'TTS', "Est-ce que vous confirmez l'ajout de cette prescription sur la liste?", 66) (4, 'user', 'UI', '/inform{"validate":"validate"}', 73) (4, 'system', 'TTS', 'Prescription validée avec succès. Traitement ajouté sur le dossier du patient', 73) (-1, 'END', 'APP', '', 73)</p><p>- N/A</p><p> </p><p>For example, in this dialogue session (00MYcyVK0t), there are two recordings.</p><p>The events are given in a single row for each dialogue session once in the</p><p>first recording (recording_0). Dialogues are described in form of events</p><p>where each action taken by the user or the system is considered as a dialogue</p><p>turn in a tuple form.</p><p> </p><p>(-1, 'START', 'APP', None, 0)</p><p> </p><p>- First element of the event is the dialogue turn number. -1 means that the application</p><p>is initialized.</p><p>- Second element describes who initiated the event: user, system, START, END</p><p>- Third element describes the type of the event: APP (start and end events)</p><p>, ASR (automatic speech recognition), TTS (text-to-speech), UI (user interface)</p><p>User clicks on buttons triggers sometimes explicit intent recognition. For</p><p>example (4, 'user', 'UI', '/inform{"validate":"validate"}') describes the</p><p>explicit intent of validation of the prescription.</p><p>- Fourth element is the timestamp (in seconds)</p><p> </p><h4>Dialogue annotations</h4><p> </p><p>We also include a manual annotation for dialogues (dialogue_annotations.txt) which indicates for each recording, if the system gave the correct answer given the utterance.</p><p> </p><p>Each line contains a keyword, either [Fail] or [OK]. The following example shows a dialogue sample with annotations:</p><p> </p><p>| dialogue_annotations.txt | paths.txt | seq.in |</p><p>|----------------------------------|----------------------------------------|--------------------------------------------------------------------------------------|</p><p>| OK | 14yHtAe555/recording_0.wav | oxytetracycline solution euh |</p><p>| OK | 14yHtAe555/recording_1.wav | oxytetracycline solution 5 gouttes matin et soir pendant 14 jours |</p><p>| Fail | 14yHtAe555/recording_2.wav | oxytetracycline solution 5 gouttes matin et soir pendant 14 jours |</p><p> </p><p>For these 3 dialogues, the dialogue annotations are accordingly OK, OK and Fail.</p><p>[Ok] means that the dialogue system reacted correctly to the input.</p><p>[Fail] means that the action of the system after this utterance should not be used</p><p>for evaluation or training. </p><p>We can notice that in the first utterance, the information are missing, however</p><p>after the second example the system normally have all of the required slots</p><p>for the prescription validation.</p><p> </p><p>It is to note that free comments added using the ASR system were noted as</p><p>Fail as these dialogues did not enter the dialogue state tracking. In this</p><p>example, the last utterance is recorded as a free comment by the prescriber</p><p>and was annotated as Fail.</p><p> </p><h4>Linking audio records to ASR events</h4><p>In order to link audio records to ASR events, the user has to use both paths.txt and events.txt</p><p> </p><p>For example for the following dialogue session (lines 1:2 of paths.txt):</p><p>00MYcyVK0t/recording_0.wav</p><p>00MYcyVK0t/recording_2.wav</p><p> </p><p>Events.txt include two ASR events:</p><p>(1, 'user', 'ASR', 'flagyl 500 mg en cachet pendant 8 jours', 30)</p><p>(3, 'user', 'ASR', '3 comprimés par jour matin midi et soir pendant 10 jours', 64)</p><p> </p><p>These ASR events corresponds to the recording files that can be found in the recordings folder.</p><p> </p><h4>Available user action annotations</h4><p> </p><p>Events.txt include annotations such as below with the following explanation:</p><p> </p><p>- /inform{"validate":"validate"} : User clicks on the validate button after seeing the prescription</p><p>- /inform{"validate":"refuse"} : User clicks on the refuse button after seeing the prescription</p><p>- ASR : User clicks on the push-to-talk button to record an utterance</p><p>- listview_item_clicked : User clicks on the list to choose a drug</p><p>- listview_cancel_clicked : User clicks on the cancel button after seeing a list of drugs</p><p>- FREE_COMMENT_ADDED : User clicks and records a free-form utterance by clicking "add free comment" button</p><p>- EMPTY_UTTERANCE: Recording containing an empty utterance</p><p>- APP_CRASH : An application crash that happened in the dialogue turn</p><p>- EVAL_FINISH_APPROVED : User clicks on the final upload button to finish the experiment</p><p>- RESTART_CONVERSATION_SESSION : User clicks on the restart conversation button</p><p>- RESTART_CANCEL_CLICKED : User cancels the restart process by clicking on the cancel button</p><p>- EVAL_FINISH_CANCELED : User cancels the final upload process by clicking on the cancel button</p><p> </p><p>** Free Comments: **</p><p>The users had the possibility of recording a speech-to-text message upon viewing a prescription.</p><p>These messages had not beed added to the dialogue state tracking but were visualized on the interface and saved in</p><p>the database. Users can find free comments by searching for FREE_COMMENT_ADDED events in the events.txt to find out</p><p>about these events.</p>
Les Clés du dialogue hiérarchique – Illustrations du livre
<p>44 visuels éditables du livre <a href="https://www.dunod.com/sciences-humaines-et-sociales/cles-du-dialogue-hierarchique-0" target="_blank" rel="noopener">Les Clés du dialogue hiérarchique – L’organisation co-construite : concilier Autorité et Autonomie</a>, co-écrit par Madeleine Laugeri, Philippe Oswald et Fabio Balli et paru le 16 octobre 2024 chez Dunod / InterEditions.</p> <p>Liste des visuels</p> <ul> <li>Diagramme flèche Planifiée</li> <li>Diagramme flèche Émergente</li> <li>Planifié et Émergent, différentes significations pour les mêmes mots</li> <li>Diagramme des logiques organisationnelles</li> <li>Diagramme du Changement Émergent</li> <li>Diagramme circulaire des flèches</li> <li>Diagramme des États du Moi (Berne)</li> <li>Diagramme de Transactions (Berne)</li> <li>Diagramme de Structure (Berne)</li> <li>Diagramme des Signes de Reconnaissance (Ernst)</li> <li>Processus aux Frontières</li> <li>Principe de délégation Émergent</li> <li>Cycle traditionnel du changement organisationnel vs Cercle Vertueux amené par le Changement Émergent</li> <li>Diagramme des Positions de Vie (Berne)</li> <li>Liens entre les Frontières et les Trois Contrats</li> <li>Temporalité des Trois Contrats</li> <li>Diagramme des Éléments Stratégiques de l’Environnement</li> <li>Diagramme des Éléments Stratégiques de l’Activité</li> <li>Dialogue organisationnel traditionnel</li> <li>Dialogue organisationnel Émergent</li> <li>Diagrammes de Contamination (Berne) et de Symbiose (Schiff et al.)</li> <li>Dynamiques saines et pathologiques</li> <li>Dynamique de la pathologie de Tyrannie</li> <li>Dynamique de la pathologie de Mutinerie</li> <li>Dynamique de la pathologie d’Usurpation de l’Émergent</li> <li>Dynamique de la pathologie d’Usurpation du Planifié</li> <li>Diagramme de Fox (Fox)</li> <li>Diagramme de Fox (Fox) et Méthode des Trois Contrats</li> <li>Scénario organisationnel (Krausz)</li> <li>Diagramme des différents niveaux</li> <li>Diagramme de structure d’un centre médico-social</li> <li>Structure de l’organisation d’un centre médico-social</li> <li>Diagramme de structure d’une organisation agricole</li> <li>Schéma des Frontières d’une organisation agricole</li> <li>Organisation d’une organisation agricole</li> <li>De la pénurie à l’abondance de reconnaissance</li> <li>Diagramme de structure avec rôles Planifié (orange) et Émergent (vert) et schéma des Trois Contrats mis en place par Noir (B)</li> <li>Méthodologie de co-création</li> <li>Exemples de communs en santé</li> <li>Diagramme du Changement Émergent Intégral</li> <li>Team Alignment Map</li> <li>Cartographie d’équipe Leonardo 3.4.5</li> <li>Les complémentarités dans Leonardo 3.4.5</li> <li>Dérouler les Trois Contrats aux divers niveaux d’une organisation</li> </ul> <p><em>Correctif 1.1 : source p. 14, diagramme p. 16</em></p>
Raw Dataset for UNFCCC SBSTA Ocean Dialogue Submission Analysis
<p>This raw dataset accompanies the manuscript titled "A New Way Forward for Ocean Climate Policy as Reflected in the UNFCCC Ocean and Climate Change Dialogue Submissions", submitted by co-authors to the journal Climate Policy. </p> <p>In order to evaluate the Ocean Dialogue submissions, we identified key themes reflected in the text and defined subtopics within each theme. Each of the 47 submissions was independently reviewed by two of the co-authors (typically one natural scientist and one with expertise in law or policy). Reviewers recorded the number of times each theme was mentioned within a submission. Totals for each submission were averaged across the two reviewers, to decrease biases from individual-level differences in annotation. The averaged counts are presented in two tables. Table OD 1 for Party submissions and Table OD2 for non Party submissions. The Non-Party acronyms are explained in Figure 1 of the manuscript text. In Table OD1, Party submissions are indicated as coming from Annex 1 Parties (i.e. More highly developed economies), Non-Annex 1 Parties, or representing Group Submissions (see UNFCCC website for further description of differentiation between Annex 1 and non-Annex 1 Parties: https://unfccc.int/parties-observers). The last table (OD Submission Length) shows the total page count for each of the 47 Ocean Dialogue submissions analyzed, ordered from shortest to longest. </p>
Dialogue Around Respiratory Illness Treatment
ClinicalTrials.gov study NCT02943551. IPD Sharing: YES. Countries: 1. Publications: 2.
Generative AI-Based Simulation for Diagnostic Communication in Type 2 Diabetes (DIALOGUE-DM2)
ClinicalTrials.gov study NCT07252193. IPD Sharing: YES. Countries: 1. Publications: 1.
Data from: Cortical reactivation of spatial and non-spatial features coordinates with hippocampus to form a memory dialogue
Open the record for dataset details and reuse information.
Data from: Dialogues in colour and behaviour: Integration of complex signalling traits and physiology
Open the record for dataset details and reuse information.
Conference Proceedings of The First International Scientific-Practical Conference- "Modern Tendencies of Dialogue in Multidenominational Society: philosophical, religious, legal view"
<p>Scientific conference</p>
Supplementary material 1 from: Hammond C (2016) Widening the circle of care: An arts-based, participatory dialogue with stakeholders on cancer care for First Nations, Inuit, and Métis peoples in Ontario, Canada. Research Ideas and Outcomes 2: e9115. https://doi.org/10.3897/rio.2.e9115
The attached file includes the evaluation of the postdoctoral fellowship application from three reviewers. Guidelines for reviewers are available online for more inforamtion (http://www.cihr-irsc.gc.ca/e/33043.html), including the rating scale that is used to score each section of the evaluation.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.