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MedDialog-FR: a French Version of the MedDialog Corpus for Multi-label Classification and Response Generation related to Women's Intimate Health
<h1>MedDialog-FR: a French Version of the MedDialog Corpus for Multi-label Classification and Response Generation related to Women's Intimate Health</h1> <p> </p> <div><strong>Contributors</strong>: Xingyu Liu, Vincent Segonne, Aidan Mannion, Didier Schwab, Lorraine Goeuriot, François Portet</div> <p> </p> <div><strong>Total Number of Single-Turn Dialogues</strong>: 16,149 dialogues of women's intimate health, 7,120 dialogues of general medicine</div> <p> </p> <div>Given the lack of French dialogue corpora for data-driven dialogue systems and the paucity of available information related to women's intimate health, MedDialog-FR is an annotated corpus of question-and-answer sessions between a patient and a doctor concerning women's intimate health. The corpus is composed of about 20,000 sessions automatically translated from the English version of MedDialog-EN. The corpus test set is composed of 1,400 sessions that have been manually post-edited and annotated with 22 categories from the UMLS ontology.</div> <p> </p> <h2>Overview of the dataset</h2> <p> </p> <div>To construct the French MedDialog Dataset (<em>MedDialog-FR</em>), we initially extracted from <em>MedDialog-EN</em> and automatically translated a total of 16,149 dialogues related to women's intimate health and an additional 7,120 dialogues related to general medicine. <em>MedDialog-EN</em> is composed of textual single-turn dialogues: a medical question by a patient and a response by a physician. From the translated dialogues, we randomly selected 900 dialogues on women's intimate health and 500 dialogues concerning general medicine to be post-edited. Subsequently, we performed multi-label annotation on the 900 questions extracted from these same dialogues focused on women's intimate health. In total, 1,286 labels were annotated, with 1.43 labels per instance in average.</div> <p> </p> <div>The summary of the statistics of the dataset:</div> <table> <tbody> <tr> <td><strong>Task</strong></td> <td><strong>Women</strong></td> <td><strong>General</strong></td> </tr> <tr> <td>Machine translation (#dialogs)</td> <td>16,149</td> <td>7,120</td> </tr> <tr> <td>Post-editing (#dialogs)</td> <td>900</td> <td>500</td> </tr> <tr> <td>Multi-label annotation (#questions)</td> <td>900</td> <td>-</td> </tr> </tbody> </table> <p> </p> <h2>Structure of the dataset</h2> <p> </p> <div>The dataset contains the following elements separated in general medicine domain (<em>MedDialog-FR-general</em>) and women's intimate health domain (<em>MedDialog-FR-women</em>):</div> <div>```</div> <div>├── MedDialog-FR-general/</div> <div>├──── machine_translation/meddialog-fr-general_machine_translation.csv</div> <div>├──── post-editing/meddialog-fr-general_post-editing.csv</div> <p> </p> <div>├── MedDialog-FR-women/</div> <div>├──── machine_translation/meddialog-fr-women_machine_translation.csv</div> <div>├──── post-editing/meddialog-fr-women_post-editing.csv</div> <div>├──── multilabel_annotation/dataset_multilabel_meddialog_22labels.csv</div> <div>├──── response_generation/dataset_response_generation_meddialog.csv</div> <div> </div> <div>```</div> <div>All the .csv files contain a column named id, which indicates the original file of *MedDialog-EN* with the id in that file. For example, hm3_96_q or hm3_96_a refers to the session with the id of 96 within the healthcaremaginc3 file. The suffix of '_q' and '_a' indicates question and answer</div> <p> </p> <h3>Machine translation</h3> <div>The .csv file contains 3 columns: id, en and fr</div> <div>- en: original question and answer in English</div> <div>- fr: translated question and answer in French</div> <p> </p> <div>Example lines:</div> <div>hm4_1121_q \t J'ai 52 ans, mes dernières règles remontent au 6 décembre, je pensais que c'était peut-être le début de la ménopause. J'ai fait un test d'urine pour la grossesse, qui s'est révélé positif, puis j'ai fait un test quantitatif de hcg 45343 (je suis infirmière et je l'ai fait au laboratoire de l'hôpital où je travaille). J'ai des crampes et des saignements (bruns) depuis 2 à 3 mois.</div> <p> </p> <div>hm4_1121_a \t Bonjour, j'ai compris votre préoccupation. Comme vous avez mentionné que le taux de bêta HCG est plus élevé, je vous suggère de faire une échographie. Cela confirmera l'âge gestationnel et la viabilité de la grossesse. Si vous tenez à poursuivre la grossesse, veuillez discuter des risques encourus avec votre gynécologue traitant. Vous pouvez également opter pour une interruption de grossesse avec des médicaments en toute sécurité jusqu'à 9 semaines de grossesse sous surveillance médicale. J'espère que cette réponse vous aidera.</div> <p> </p> <h3>Post-editing</h3> <div>The .csv file contains 3 columns: id, machine_translation and post-edited</div> <p> </p> <div>Example line:</div> <div>hm4_3334_q \t bonjour docteur je suis atteinte de pcos, je me suis mariée en novembre 2011.nous essayons d'avoir une grossesse depuis deux mois ... et ma question est comment savoir la sévérité du pcos et quel est le meilleur moment pour concv \t bonjour docteur, je suis atteinte de SOPK, je me suis mariée en novembre 2011. Nous essayons de concevoir depuis deux mois ... et ma question est comment savoir la sévérité du SOPK et quel est le meilleur moment pour concevoir.</div> <p> </p> <h3>Multi-label annotation</h3> <div>The .csv file contains 5 columns: id, source_file, labels and split.</div> <div>- source_file: the source file where the text content for classification can be found with id</div> <div>- labels: UMLS IDs representing expert-validated labels for classification</div> <div>- split: train, dev or test</div> <p> </p> <div>Example line:</div> <div>hm1_33568_q \t '../post-editing/meddialog-fr-women_post-editing.csv' \t ['C0700589', 'C0227791'] \t train</div> <p><br><br></p> <div><strong>Partitioning</strong>:</div> <div>We split the <em>MedDialog-FR-women</em> multi-label dataset into a training set of 500 instances, a validation set of 100 instances and a test set of 300 instances. The ratio was chosen to balance the need for maximizing the amount of fine-tuning data available while also ensuring that the test set is large enough for the results to be statistically significant, given the scarcity of some categories. The split statistics are summarized in the following table. To maintain consistent label distribution, we leveraged the iterative stratification algorithm during the data splitting process.</div> <table> <tbody> <tr> <td><strong>Split</strong></td> <td><strong>#Questions</strong></td> </tr> <tr> <td>Train</td> <td>500</td> </tr> <tr> <td>Validation</td> <td>100</td> </tr> <tr> <td>Test</td> <td>300</td> </tr> </tbody> </table> <p> </p> <h3>Response generation</h3> <div>The .csv file contains 3 columns: id, split and source_file</div> <div>- split: train, dev or test</div> <div>- source_file: the source file where the text content for response generation can be found with id</div> <div><strong>Partitioning:</strong></div> <div>The validation and test data contain the same session ID as the multi-label validation and test, but they include the corresponding answers. for the training set, we use the same ones as multi-label dataset plus the machine translated sessions.</div> <div> </div> <table> <tbody> <tr> <td><strong>Split</strong></td> <td><strong>#Dialogues</strong></td> </tr> <tr> <td>Train</td> <td>15,749</td> </tr> <tr> <td>Validation</td> <td>100</td> </tr> <tr> <td>Test</td> <td>300</td> </tr> </tbody> </table> <div> </div> <p><br><br></p> <h2>Corpus data cleaning</h2> <div>By examining the MedDialog-EN corpus, we identified data that could potentially leak personal information such as the first and last name, email address, URL, etc. In order to safeguard privacy, we conducted a series of data cleaning procedures, especially anonymization:</div> <div>1. replace URLs with #URL# (regex pattern: `https?:\/\/(www\.)?[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)</div> <div>`)</div> <div>2. replace emails with #EMAIL# (regex pattern: `^[\w-\.]+@([\w-]+\.)+[\w-]{2,4}`)</div> <div>3. replace phone numbers with #TEL# (regex pattern: `^[\+]?[(]?[0-9]{3}[)]?[-\s\.]?[0-9]{3}[-\s\.]?[0-9]{4,6}`)</div> <div>4. replace dates with #DATE# (regex patterns: `\d{1,2}\/\d{1,2}\/\d{2,4}</div> <div>`; `(Jan(?:uary)?|Feb(?:ruary)?|Mar(?:ch)?|Apr(?:il)?|May|Jun(?:e)?|Jul(?:y)?|Aug(?:ust)?|Sep(?:tember)?|Oct(?:ober)?|Nov(?:ember)?|Dec(?:ember)?)\s+(\d{1,2})\s+(\d{4})`)</div> <div>5. replace hospital or clinic names with #HOSPITAL# (text patterns: `clinic`; `hospital`)</div> <div>6. replace names in questions with #Person1#, and names in answers with #Person2#. If there is a name in an answer identical to the name in its question, replace it with #Person1#. (text patterns: `I am`; `I'm`;`Dr`; `Doctor`)</div> <div>7. replace the names of data source forums with coded letters (text patterns: forum names)</div> <p><br><br></p> <h2>Ethics Statement and Limitations</h2> <div>Access to actual medical data is very restricted and protected in France. We thus used an already publicly available corpus in English. But we did not simply translate it. We first made sure that no personal information could be found in the data. This is why we replaced all names that could have been kept in the original data. We also performed post-edition after automatic translation to adapt the phrasing and medical term to the French culture. All people recruited for annotation were treated fairly. This includes, but is not limited to, compensating them fairly and ensuring that they were voluntary participants. We do not foresee any direct social consequences or ethical issues.</div> <p> </p> <div>Authors of MedDialog were warned at our project and answered our questions.</div> <p> </p> <div>Since the original corpus is derived from dialogues in the U.S.A., there might be some cultural differences with French-speaking countries in the way people interact with doctors and which treatments and medical advises can be provided.</div> <p> </p> <div>Answers to questions should not be applied for self-treatment.</div> <div> </div>
Labeled Datasets for Research on Information Operations
<h1><strong>Labeled Datasets for Research on Information Operations</strong></h1> <h2><strong>Compliance with Platform Terms<br></strong></h2> <p>To comply with the platform terms, we ask that you download one data file per researcher, per day. By requesting access, you agree to abide by these rules.</p> <h2><strong>Data Sharing Policy</strong></h2> <p>Following university, security, and ethical guidelines, we are unable to respond to data requests from research institutions that are affiliated with foreign military organizations or from a list of countries of concern (currently China, North Korea, Iran, Russia, Cuba, Syria, and Venezuela).</p> <h2><strong>README</strong></h2> <p>19-November-2024<br>Contact: <a href="https://osome.iu.edu/">Observatory on Social Media</a></p> <p><strong>Dataset Articles</strong><br>This dataset is collected and processed according to the paper "<a href="https://doi.org/10.48550/arXiv.2411.10609">Labeled Datasets for Research on Information Operations</a>."</p> <p><strong>Description</strong><br>These datasets contain data curated for research on information operations (IO) and includes both labeled IO and control data. The datasets cover 26 verified IO campaigns from various countries and provide comprehensive records of posts from IO accounts alongside control posts from legitimate accounts discussing similar topics during the same periods. The datasets enable the development and benchmarking of IO detection methods by comparing coordinated versus organic accounts.</p> <p><strong>License</strong><br>This dataset is available under the <a href="https://creativecommons.org/licenses/by-nc-nd/4.0/">Attribution-NonCommercial-NoDerivatives 4.0 International</a> license. If you use this data, please cite the original paper.</p> <p><strong>Dataset Content</strong><br>The dataset includes anonymized fields to preserve privacy, and is structured with the following columns:</p> <ul> <li>postid: Unique identifier for each post within the dataset.</li> <li>post_text: The textual content of the post. The PII inside post_text such as mentions and URLs are hashed</li> <li>application_name: Hashed version of the name of the application or platform from which the post was made.</li> <li>post_language: Language in which the post was written.</li> <li>in_reply_to_postid: Anonymized ID of the post this entry is replying to, if applicable.</li> <li>in_reply_to_accountid: Anonymized ID of the account the post is replying to, if applicable.</li> <li>post_time: Timestamp indicating when the post was made.</li> <li>accountid: Unique anonymized ID for the account that created the post.</li> <li>account_profile_description: Description provided by the account holder in their profile.</li> <li>follower_count: Number of followers the account had at the time of data collection.</li> <li>following_count: Number of accounts the user was following at the time of data collection.</li> <li>account_creation_date: Date when the account was created.</li> <li>is_repost: Boolean indicator if the post is a repost.</li> <li>reposted_accountid: Anonymized ID of the original account that made the reposted post, if applicable.</li> <li>reposted_postid: Anonymized ID of the original post that was reposted, if applicable.</li> <li>hashtags: Hashtags included in the post content, if any.</li> <li>urls: Hashed URLs shared within the post, if any.</li> <li>account_mentions: Anonymized ID of accounts mentioned within the post, if any.</li> <li>is_control: Boolean indicator marking whether the post is from a control (True) or IO (False) account.</li> </ul> <p>Data for different campaigns are organized in separate versions of this repository, which can be also found below or in the excel file shared.</p> <table> <tbody> <tr> <td><strong>Campaign Name</strong></td> <td><strong>URL</strong></td> </tr> <tr> <td>Armenia</td> <td><a href="https://doi.org/10.5281/zenodo.14141550">https://doi.org/10.5281/zenodo.14141550</a></td> </tr> <tr> <td>Bangladesh</td> <td><a href="https://doi.org/10.5281/zenodo.14188947">https://doi.org/10.5281/zenodo.14188947</a></td> </tr> <tr> <td>Catalonia</td> <td><a href="https://doi.org/10.5281/zenodo.14188959">https://doi.org/10.5281/zenodo.14188959</a></td> </tr> <tr> <td>China_1</td> <td><a href="https://doi.org/10.5281/zenodo.14188970">https://doi.org/10.5281/zenodo.14188970</a></td> </tr> <tr> <td>China_2</td> <td><a href="https://doi.org/10.5281/zenodo.14188975">https://doi.org/10.5281/zenodo.14188975</a></td> </tr> <tr> <td>Cuba Part 1</td> <td><a href="https://doi.org/10.5281/zenodo.14188984">https://doi.org/10.5281/zenodo.14188984</a></td> </tr> <tr> <td>Cuba Part 2</td> <td><a href="https://doi.org/10.5281/zenodo.14189008">https://doi.org/10.5281/zenodo.14189008</a></td> </tr> <tr> <td>Ecuador</td> <td><a href="https://doi.org/10.5281/zenodo.14189015">https://doi.org/10.5281/zenodo.14189015</a></td> </tr> <tr> <td>Egypt_UAE</td> <td><a href="https://doi.org/10.5281/zenodo.14189018">https://doi.org/10.5281/zenodo.14189018</a></td> </tr> <tr> <td>Ghana_Nigeria</td> <td><a href="https://doi.org/10.5281/zenodo.14189028">https://doi.org/10.5281/zenodo.14189028</a></td> </tr> <tr> <td>Iran_1</td> <td><a href="https://doi.org/10.5281/zenodo.14189037">https://doi.org/10.5281/zenodo.14189037</a></td> </tr> <tr> <td>Iran_2</td> <td><a href="https://doi.org/10.5281/zenodo.14189038">https://doi.org/10.5281/zenodo.14189038</a></td> </tr> <tr> <td>Iran_3</td> <td><a href="https://doi.org/10.5281/zenodo.14189041">https://doi.org/10.5281/zenodo.14189041</a></td> </tr> <tr> <td>Iran_4</td> <td><a href="https://doi.org/10.5281/zenodo.14189047">https://doi.org/10.5281/zenodo.14189047</a></td> </tr> <tr> <td>Iran_5</td> <td><a href="https://doi.org/10.5281/zenodo.14189048">https://doi.org/10.5281/zenodo.14189048</a></td> </tr> <tr> <td>Iran_6</td> <td><a href="https://doi.org/10.5281/zenodo.14189053">https://doi.org/10.5281/zenodo.14189053</a></td> </tr> <tr> <td>Qatar</td> <td><a href="https://doi.org/10.5281/zenodo.14189058">https://doi.org/10.5281/zenodo.14189058</a></td> </tr> <tr> <td>Russia_1</td> <td><a href="https://doi.org/10.5281/zenodo.14189061">https://doi.org/10.5281/zenodo.14189061</a></td> </tr> <tr> <td>Russia_2</td> <td><a href="https://doi.org/10.5281/zenodo.14189072">https://doi.org/10.5281/zenodo.14189072</a></td> </tr> <tr> <td>Russia_3</td> <td><a href="https://doi.org/10.5281/zenodo.14189075">https://doi.org/10.5281/zenodo.14189075</a></td> </tr> <tr> <td>Russia_4</td> <td><a href="https://doi.org/10.5281/zenodo.14189078">https://doi.org/10.5281/zenodo.14189078</a></td> </tr> <tr> <td>Russia_5</td> <td><a href="https://doi.org/10.5281/zenodo.14189081">https://doi.org/10.5281/zenodo.14189081</a></td> </tr> <tr> <td>Spain</td> <td><a href="https://doi.org/10.5281/zenodo.14189086">https://doi.org/10.5281/zenodo.14189086</a></td> </tr> <tr> <td>Thailand</td> <td><a href="https://doi.org/10.5281/zenodo.14189095">https://doi.org/10.5281/zenodo.14189095</a></td> </tr> <tr> <td>UAE</td> <td><a href="https://doi.org/10.5281/zenodo.14189098">https://doi.org/10.5281/zenodo.14189098</a></td> </tr> <tr> <td>Venezuela_1</td> <td><a href="https://doi.org/10.5281/zenodo.14189107">https://doi.org/10.5281/zenodo.14189107</a></td> </tr> <tr> <td>Venezuela_2</td> <td><a href="https://doi.org/10.5281/zenodo.14189110">https://doi.org/10.5281/zenodo.14189110</a></td> </tr> </tbody> </table>
Tracking label images with TrackMate
<p>Dataset for the tutorial showing how to track label images with TrackMate.</p> <p>For more information see https://imagej.net/plugins/trackmate/trackmate-label-image-detector</p> <p> </p>
Code & Data for "Vocal labeling of others by nonhuman primates"
<p>This dataset contains code and data for the figures associated with the paper:</p> <p>"<strong>Vocal labeling of others by nonhuman primates</strong>" </p> <p>Oren <em>et al.</em>, Science (2024) [DOI: <a href="https://www.science.org/doi/10.1126/science.adp3757">10.1126/science.adp3757</a>]</p> <p>The repository includes:</p> <ul> <li>MATLAB code for analysizing phee calls.</li> <li>Data from the main figures of the paper.</li> </ul> <p>Any questions should be directed to the corresponding author of the paper, David Omer (<a rel="noreferrer">david.omer@mail.huji.ac.il</a>).</p>
Plot-level semantically labelled terrestrial laser scanning point clouds
<p><strong>Abstract</strong></p> <p>Point clouds from Terrestrial Laser Scanning (TLS) are an increasingly popular source of data for studying plant structure and function. However, unlocking their full potential currently requires extensive manual processing to extract ecologically important information. One key task is the accurate semantic segmentation of different plant material within point clouds, particularly wood and leaves, which is required to understand plant productivity, architecture, competition, space optimisation and physiology, and is a key step in common approaches to individual tree extraction. Existing automated semantic segmentation methods are primarily developed for single ecosystem types, and whilst they show good accuracy for biomass assessment from the trunk and large branches, often perform less well within the crown. In this study, we demonstrate a new framework that uses a deep learning architecture developed from PointNet++ and pointNEXT for processing 3D point clouds to provide a reliable semantic segmentation of wood and leaf in TLS point clouds from the tree base to branch tips, applied to diverse natural European forests. Our model combines meticulously labelled data with voxel-based sampling and a novel gated reflectance integration module embedded throughout the feature extraction layers. We evaluate its performance across an extensive dataset, encompassing diverse ecosystem types and sensor characteristics. Our results show consistent outperformance against the most widely used PointNet++-based approach for leaf/wood segmentation on a high-density TLS dataset collected across diverse mixed forest plots across all major biomes in Europe. We tested our model against others’ open data from China, Eastern Cameroon, Germany and Finland, collected using both time-of-flight and phase-shift sensors, finding consistently strong performance, showcasing the transferability of our model to a wide range of ecosystems and sensors. Our newly developed evaluation metric for assessing performance in the outer parts of the canopy, such as in twigs and small branches, found our model to clearly outperform the most widely used approach.</p> <p><strong>Methods</strong></p> <p>Within each country, we scanned a subset of the 30 m x 30 m FUNDIV plots using a Riegl VZ400i TLS instrument (RIEGL Gmbh, Horn, Austria), scanning at 600MHz and with an angular resolution of 0.04 mrad. All plots were scanned following a 10m grid system with a minimum of 16 upright and 16 tilt scans (following Wilkes et al. 2017), with additional scans to minimise occlusion in dense areas, and on the plot perimeter. To ensure high-quality data with minimal noise, scanning was paused when wind conditions rose above 5 m/s (measured with an anemometer on the ground) or when gusts were visually evident. In order to create our labelled dataset, we used a semi-automated approach informed by existing approaches followed by significant manual cleaning. Vicari et al. (2019) found anisotropy, verticality and linearity to be informative features for leaf-wood separation, so we created these geometric features at spatial scales of approx. 5 cm - 0.5 m (using CloudCompare, 2023). Alongside these, we used reflectance and xyz information for each point and labelled leaf-wood by thresholding these features. We followed this with intensive manual checking and cleaning to ensure high label quality, especially in the smaller branches and twigs. Our dense scanning and labelling strategy means that this dataset is an ideal candidate for training and testing the capabilities of processing algorithms. Our dataset comprises nine 10 m x 10 m blocks of x, y, z coordinates with corresponding reflectance values and labels, all at 1 cm resolution achieved through voxel downsampling. This data was used for training and validation. For comprehensive evaluation, we incorporated additional openly available datasets, which we cropped to reduce size and cleaned to rectify erroneous labels. The original, unmodified versions of these datasets can be accessed as follows:</p> <div> <p>*Mspace Lab (2024) ‘ForestSemantic: A Dataset for Semantic Learning of Forest from Close-Range Sensing’, Geo-spatial Information Science. Zenodo. <a href="https://doi.org/10.5281/zenodo.13285640">https://doi.org/10.5281/zenodo.13285640</a>. Distributed under a Creative Commons Attribution Non Commercial No Derivatives 4.0 International licence.</p> <p>Wang, Di; Takoudjou, Stéphane Momo; Casella, Eric (2021). LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.np5hqbzp6">https://doi.org/10.5061/dryad.np5hqbzp6</a>. Distributed under a Creative Commons 0 1.0 Universal licence.</p> <p>Wan, Peng; Zhang, Wuming; Jin, Shuangna (2021). Plot-level wood-leaf separation for terrestrial laser scanning point clouds [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.rfj6q5799">https://doi.org/10.5061/dryad.rfj6q5799</a>. Distributed under a Creative Commons CC0 1.0 Universal licence.</p> <p>Weiser, Hannah; Ulrich, Veit; Winiwarter, Lukas; Esmorís, Alberto M.; Höfle, Bernhard, 2024, "Manually labeled terrestrial laser scanning point clouds of individual trees for leaf-wood separation", <a href="https://doi.org/10.11588/data/UUMEDI">https://doi.org/10.11588/data/UUMEDI</a>, heiDATA, V1, UNF:6:9U7BGTgjjsWd1GduT1qXjA== [fileUNF]. Distributed under a Creative Commons Attribution 4.0 International Deed.</p> </div> <p>*For licensing reasons these data re not included in this repository but can be downladed from the doi provided. </p> <p> </p> <p> </p>
Single-arm, open-label, multicenter first in human study to evaluate the safety and performance of Dura Sealant Patch in reducing CSF leakage following elective cranial surgery: The ENCASE trial
<p><span><span><span><span><span><span><span><span><span><span><span><b>Objective:</b> The Dural Sealant Patch (DSP) is designed for watertight dural closure after cranial surgery. The goal of this study is to assess, for the first time, safety and performance of the DSP as a means of reducing CSF leakage in patients undergoing elective cranial intradural surgery with a dural closure procedure.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Design:</b> First in human, open-label, single-arm, multicenter study with 360 days (12 months) follow up. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Setting: </b>Three large tertiary reference neurosurgical centers, 2 in the Netherlands and 1 in Switzerland. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Participants</b>: Forty patients undergoing elective cranial neurosurgical procedures, stratified into 34 supra- and 6 infratentorial trepanations.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Intervention:</b> Each patient received one DSP after cranial surgery and closure of the dura mater with sutures.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Outcome measures:</b> Primary composite endpoint was occurrence of one of the following events: postoperative percutaneous CSF leakage, intraoperative leakage at 20 cm H2O positive end-expiratory pressure or postoperative wound infection. Overall success was defined as achieving the primary endpoint in no more than 2 patients. Secondary endpoints were device related serious adverse events (SAEs) or adverse events (AEs), pseudomeningocele and thickness of dura + DSP. Additional endpoints were reoperation in 30 days and user satisfaction. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results:</b> No patients met the primary endpoint. No device related (S)AEs were observed. There were two incidences of self-limiting pseudomeningocele as confirmed on MRI. Thickness of dura and DSP were (mean +/- SD) 3.5 mm +/- 2.0 at day 7 and 2.1 mm +/- 1.2 at day 90. No patients were re-operated within 30 days. Users reported a satisfactory design and intuitive application.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><strong>Conclusions: </strong>DSP, later officially named Liqoseal, is a safe and potentially efficacious device for reducing CSF leakage after intracranial surgery, with favorable clinical handling characteristics. A randomized controlled trial is needed to assess Liqoseal efficacy against the best current practice for reducing postoperative CSF leakage.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Strengths and limitations</b></span></span></span></span></span></span></span></span></span></span></span></p> <ul> <li>The trial studies a device to prevent postoperative cerebrospinal fluid (CSF) leakage, which is one of the most common neurosurgical complications.</li> <li>The study protocol was performed in multiple centers, registered, pre-published and strictly followed.</li> <li>The composite endpoint of the trial reduced the number of inclusions needed.</li> <li>The study did not involve a comparison to current clinical standard and has a potential selection bias, so generalization of results with regard to DSP efficacy needs to be cautiously undertaken.</li> </ul>
Human-labeled corpus of news articles, containing issue and game frames
<p>A human-labeled corpus of online news articles from New York Times and Los Angeles Times, containing issue and game frames, annotated with a great range of linguistic features at the following levels: syntactic (both form and function levels), semantic, semantic-syntactic, and pragmatic. Our specialized corpus has been created carefully to represent the written online media language regarding political news on the following topics: USA elections 2020, Donald Trump's impeachment, the Armenian genocide, Greta Thunberg, and Taiwan's elections. In total, 100 news articles were extracted, including image descriptions and titles that seemed relevant for our analysis. </p>
Figure 4 from: Drinkwater R, Cubey R, Haston E (2014) The use of Optical Character Recognition (OCR) in the digitisation of herbarium specimen labels. PhytoKeys 38: 15-30. https://doi.org/10.3897/phytokeys.38.7168
Figure 4 - Digitiser responses to questions 1, 2 and 3 of survey
Figure 3 from: Drinkwater R, Cubey R, Haston E (2014) The use of Optical Character Recognition (OCR) in the digitisation of herbarium specimen labels. PhytoKeys 38: 15-30. https://doi.org/10.3897/phytokeys.38.7168
Figure 3 - Box plot of Partial Protocol results.
Tracing low-CO2 fluxes in incubation and 13C labeling experiments; data set
<p>Data set containing data from feature tests (1-3) as well as photosynthesis and respiration measurements.</p> <p> </p>
C3 weakly labelled protein crystallisation data
<p>Set of weakly labelled protein crystallisation images which were used to augment the MARCO dataset for training in <a href="https://www.biorxiv.org/content/10.1101/2022.09.28.509867">Moving beyond MARCO</a>.</p> <p>Archive has been split into max 1GB chunks. Need to be joined together before extracting</p> <pre><code class="language-bash">cat c3_local_images.tar.xz.part* > c3_local_images.tar.xz</code></pre> <p> </p>
Dataset for "Label-free ghost cytometry for manufacturing of cell therapy products"
<p>Dataset for "Label-free ghost cytometry for manufacturing of cell therapy products"</p>
An Open Label Dose Titration of Sevelamer Carbonate Tabs 3 Times a Day in Hyperphosphatemic CKD Patients Not On Dialysis
ClinicalTrials.gov study NCT00681941. IPD Sharing: Not stated. Countries: 5. Publications: 0.
An Open-label, Multidose Dose-escalation Study to Understand the Safety of CRISPR Gene-editing Therapy and Its Long-Lasting Effects in DMD Patients (MUSCLE)
ClinicalTrials.gov study NCT06594094. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Clinical Study of 68Ga-labeled Novel Nectin-4 Bicyclic Peptide PET/CT for Imaging of Solid Tumors With High Nectin-4 Expression
ClinicalTrials.gov study NCT07319130. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Prospective, Open-label Study of Seraph 100 in Patients With Prolonged COVID (PC)
ClinicalTrials.gov study NCT06637800. IPD Sharing: NO. Countries: 1. Publications: 0.
Phase I, Open-label, Dose-escalation, Safety and PK Study of AZD4877
ClinicalTrials.gov study NCT00471367. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Open-label Extension Study for CLCZ696G2301 (PARADISE-MI)
ClinicalTrials.gov study NCT04637555. IPD Sharing: YES. Countries: 0. Publications: 0.
Open Label, Multi-Center Study, Evaluating the Effect of Adipose Tissue Processed With the SyntrFuge™ System for Facial Fat Grafting
ClinicalTrials.gov study NCT05522426. IPD Sharing: NO. Countries: 1. Publications: 0.
A Single-arm, Open-label, Single-center Prospective Study of Fruquintinib Combined With Envafolimab in the Treatment of Advanced or Unresectable Locally Advanced Bone and Soft Tissue Sarcoma
ClinicalTrials.gov study NCT05941325. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
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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.