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289 results for “Licenses”
WorldCereal open global harmonized reference data repository (CC-BY licensed data sets)
<p>Within the <strong>ESA funded </strong>WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets.</p> <p>This publication includes those harmonized data sets of which the original data set was published under the CC-BY license or a license similar to CC-BY. See document "_In-situ-data-World-Cereal - license - CC-BY.pdf" for an overview of the original data sets. </p>
Source data for "Regulation of replication origin licensing by ORC phosphorylation reveals a two-step mechanism for Mcm2-7 ring closing"
<p>Source data for "Regulation of replication origin licensing by ORC phosphorylation reveals a two-step mechanism for Mcm2-7 ring closing" </p> <p>The data is organized by Figure and associated Supp Figure(s). A README file is included in each figure folder to explain the files.</p> <p>(note: Data is included for Figs2-7 and SuppFigs 2-8. Fig.1 and SuppFig.1 did not have any associated data matrices, so there is no upload for them here). </p> <p>Briefly, the single molecule data is included in several different formats, all generated from single-molecule TIRF-microscopy video files using Matlab:</p> <p>Integrated trace files "traces" include integrated fluorescence intensity at individual DNA molecules over a 20 minute reaction.</p> <p>Background-corrected trace files normalize the integrated fluorescence intensity to a local-average background, as described in the Methods section of the paper-- these are used for EFRET calculations.</p> <p>"Intervals" files include the start, end time, and duration of protein-DNA interactions and were generated from the trace files and the raw videos using the Imscroll program (available at: https://github.com/gelles-brandeis/CoSMoS_Analysis).</p> <p>EFRET vectors are concatenated vectors of the EFRET vs time values of all protein-DNA interactions in the experiment-- these are used to generate EFRET heat maps. </p> <p>Matlab analysis scripts used in the paper are uploaded in a separate folder "ALA_scripts_used_final". Several of these scripts reference custom Matlab functions from the Gelles lab which are available here: https://github.com/gelles-brandeis/jganalyze and should be downloaded along with my attached scripts file. </p> <p>note: Raw single-molecule video files have not been uploaded here due to large sizes, but can be provided upon request. </p>
License Flowers
<p><strong>License Flowers</strong></p><p>Carlos Vivar Rios, Open Research Data Engineer, Swiss Data Science Center</p><p>Cyril Matthey–Doret, Sr. Data Science Engineer, Swiss Data Science Center</p><p>Stefan Milosavljevic, Biomedical Data Engineer, Swiss Data Science Center</p><p><i>A joint venture between ETH Zurich, the EPFL and the Paul Scherrer Institute, the Swiss Data Science Center was created in 2017. The center's mission is to accelerate the use of data science and machine learning techniques within academic disciplines of the ETH Domain, the Swiss academic community at large, public institutions, and the industrial sector, and enable data-driven science and innovation for societal impact.</i></p><p><strong>What we do in our work:</strong> we're all data engineers, creating infrastructure to make the life of people analyzing data easier (usually data scientists). We're in the Open Research Data team, using open-source code and sharing open-source code following best practices to ensure long-term (re)usability, also known as <a href="https://www.go-fair.org/fair-principles/">FAIR principles</a>. Our professional positions and contributions are based on all the open-source code and material available online, which is why licenses play a fundamental role in our day-to-day work.</p><p><strong>Project Overview:</strong></p><p><strong>Idea: </strong>Every code license is extremely abstract and dull, but beyond this dullness lies a world where others can (re)use code without legal issues, promoting progress, collaboration and personal rights. The goal of the artwork is to bring each license to life, highlighting their beauty and promote their use. We started by imagining open-source software development as an ecosystem of living organisms with code as genetic information. For this ecosystem to thrive, pieces of code are used, adapted, shared, or refactored, all depending on the license behind. Posting your code on the internet is not enough: without a license specifying the terms of use, it defaults to "all rights reserved", meaning no-one is allowed to use, redistribute or adapt it. Following the biological metaphor, if code is DNA, then licenses are like organs defining whether codes are compatible and fertile. Similar to different species, some licenses are more promiscuous than others, and therefore they follow different reproductive strategies. </p><p>Flowers encapsulate our metaphor perfectly as they are reproductive organs of plants, and they're also used to represent fertility in humans. In our art piece, we decided to use this strong symbol by associating each license to one flower. As for real flowers, we reimagined each code license as a unique character with its own story, personality, and quirks. These characters are brought to life through intricate and evocative drawings, seamlessly integrated into the pages of real notebooks. These drawings are inspired by the organic world of biology, specifically flowers, following the style of old biology books. </p><p><strong>Objective:</strong> Visualize the licenses used in a representative sample of repositories from papers with code (https://paperswithcode.com/)</p><p><strong>Steps:</strong></p><p><i>1. Data Collection & Cleaning:</i></p><p>We began by collecting information from 220 repositories (14000 in the second iteration) from papers with code. We cleaned the dataset from repos that did not contain code.</p><p><i>2. Data Aggregation and Use:</i></p><p>The cleaned data from the second iteration was then aggregated based on license type and programming language. Then we selected the five most common types of license: no license, MIT, BSD-3 Clause, GPL 3.0 and Apache. We also added the artistic license Creative Commons, even though rare, to highlight diversity. </p><p><i>3. Visualization Approach:</i></p><p>We wanted to obtain two AI-driven inputs: a seed image, and a prompt. The former will provide guidance to the AI algorithm for styling and feeling, while the latter will drive the generation towards a specific composition and design. </p><p>For the seed image, instead of opting for a conventional plotting library, we drew inspiration from the unique visualization style of Dear Data (http://www.dear-data.com/). This led us to design a hand-drawn doodle, reminiscent of a flower, as the seed for style. </p><p>For the prompt, we compiled the different features for each of the selected licenses from <a href="https://www.tldrlegal.com/">https://www.tldrlegal.com/</a>. This resource describes license features into what can/must be done and cannot. With all this profiling, we used ChatGPT and asked for describing flowers that represent those traits. The results were one prompt per license with guides for the color, the shape of the stem, the petals, and so on. </p><p>After refinement of both inputs, we used Midjourney to obtain suggestions of different flowers. Then we used inpainting, upscaling and cropping methods to refine the images. As a final touch to the flower pictures, we also added their names written in cursive using the online tool <a href="https://www.calligrapher.ai/">https://www.calligrapher.ai/</a>, trying to match the writing style to each license character.</p><p><i>4. Physical display:</i></p><p>We selected 6 different kinds of physical formats to print each flower. Each format represents the personality of the people who might be interested in each license. For example, the CC license flower was assigned to a sketchbook, since it's an artistic license. For MIT, since it's a very relaxed and popular license, we chose a simple notebook with blank pages. For Apache, a license with many terms that protects the recognition for contributions, we chose a black notebook with lined pages, to highlight its professional and precise nature. GPL can be seen as a passionate license, trying to spread its wisdom to future code, so we picked a notebook with a brown soft cover, better suited for multiple use. BSD-3 is similar to MIT, but with some details about what needs to be mentioned when changing things, similar to a friendly guide, so we picked a purple notebook with grid paper, trying to give structure but still leaving you the freedom of use. Finally, the flower for no license was printed on an A3 poster, as it represents code that could have been in a notebook, but is not. </p><p>Only the MIT license is in an A4 format notebook, since it's the most popular license by far. All other licenses are in A5. To highlight the vast majority of code with no license, the no license poster is in A3 format. The poster will be displayed on an easel, overlooking the license flowers and showing its dominance over them.</p>
Study Describing the Immunogenicity and Safety of Quadrivalent Recombinant Influenza Vaccine (RIV4) Versus a Licensed Quadrivalent-inactivated Influenza Vaccine (IIV4) (Fluarix® Quadrivalent) in Parti
ClinicalTrials.gov study NCT05144945. IPD Sharing: YES. Countries: 1. Publications: 1.
Study to Evaluate the Immune Response After a Booster Dose of a Quadrivalent Meningococcal (MenACYW) Conjugate Vaccine When Administered Alone or Concomitantly With a Licensed Meningococcal Serogroup
ClinicalTrials.gov study NCT04084769. IPD Sharing: YES. Countries: 2. Publications: 1.
Data for: Cdt1 inhibits CMG helicase in early S phase to separate origin licensing from DNA synthesis
Open the record for dataset details and reuse information.
Dataset of the paper: "How do Hugging Face Models Document Datasets, Bias, and Licenses? An Empirical Study"
<p>This replication package contains datasets and scripts related to the paper: "*How do Hugging Face Models Document Datasets, Bias, and Licenses? An Empirical Study*"</p><p> </p><p>## Root directory</p><p>- `statistics.r`: R script used to compute the correlation between usage and downloads, and the RQ1/RQ2 inter-rater agreements</p><p>- `modelsInfo.zip`: zip file containing all the downloaded model cards (in JSON format)</p><p>- `script`: directory containing all the scripts used to collect and process data. For further details, see README file inside the script directory.</p><p> </p><p>## Dataset</p><p>- `Dataset/Dataset_HF-models-list.csv`: list of HF models analyzed</p><p>- `Dataset/Dataset_github-prj-list.txt`: list of GitHub projects using the *transformers* library</p><p>- `Dataset/Dataset_github-Prj_model-Used.csv`: contains usage pairs: project, model</p><p>- `Dataset/Dataset_prj-num-models-reused.csv`: number of models used by each GitHub project</p><p>- `Dataset/Dataset_model-download_num-prj_correlation.csv` contains, for each model used by GitHub projects: the name, the task, the number of reusing projects, and the number of downloads</p><p> </p><p> </p><p>## RQ1</p><p>- `RQ1/RQ1_dataset-list.txt`: list of HF datasets</p><p>- `RQ1/RQ1_datasetSample.csv`: sample set of models used for the manual analysis of datasets</p><p>- `RQ1/RQ1_analyzeDatasetTags.py`: Python script to analyze model tags for the presence of datasets. it requires to unzip the `modelsInfo.zip` in a directory with the same name (`modelsInfo`) at the root of the replication package folder. Produces the output to stdout. To redirect in a file fo be analyzed by the `RQ2/countDataset.py` script</p><p>- `RQ1/RQ1_countDataset.py`: given the output of `RQ2/analyzeDatasetTags.py` (passed as argument) produces, for each model, a list of Booleans indicating whether (i) the model only declares HF datasets, (ii) the model only declares external datasets, (iii) the model declares both, and (iv) the model is part of the sample for the manual analysis</p><p>- `RQ1/RQ1_datasetTags.csv`: output of `RQ2/analyzeDatasetTags.py`</p><p>- `RQ1/RQ1_dataset_usage_count.csv`: output of `RQ2/countDataset.py`</p><p> </p><p> </p><p>## RQ2</p><p>- `RQ2/tableBias.pdf`: table detailing the number of occurrences of different types of bias by model Task</p><p>- `RQ2/RQ2_bias_classification_sheet.csv`: results of the manual labeling</p><p>- `RQ2/RQ2_isBiased.csv`: file to compute the inter-rater agreement of whether or not a model documents Bias</p><p>- `RQ2/RQ2_biasAgrLabels.csv`: file to compute the inter-rater agreement related to bias categories</p><p>- `RQ2/RQ2_final_bias_categories_with_levels.csv`: for each model in the sample, this file lists (i) the bias leaf category, (ii) the first-level category, and (iii) the intermediate category</p><p> </p><p> </p><p>## RQ3</p><p>- `RQ3/RQ3_LicenseValidation.csv`: manual validation of a sample of licenses</p><p>- `RQ3/RQ3_{NETWORK-RESTRICTIVE|RESTRICTIVE|WEAK-RESTRICTIVE|PERMISSIVE}-license-list.txt`: lists of licenses with different permissiveness</p><p>- `RQ3/RQ3_prjs_license.csv`: for each project linked to models, among other fields it indicates the license tag and name</p><p>- `RQ3/RQ3_models_license.csv`: for each model, indicates among other pieces of info, whether the model has a license, and if yes what kind of license</p><p>- `RQ3/RQ3_model-prj-license_contingency_table.csv`: usage contingency table between projects' licenses (columns) and models' licenses (rows)</p><p>- `RQ3/RQ3_models_prjs_licenses_with_type.csv`: pairs project-model, with their respective licenses and permissiveness level</p><p> </p><p>## scripts</p><p>Contains the scripts used to mine Hugging Face and GitHub. Details are in the enclosed README</p><p> </p>
The Paradox of Innovation Non-Disclosure: Evidence from Licensing Contracts - Miscellaneous Public Data
<p>Various datasets</p> <p> </p> <ol> <li>Hoberg Phillips data library</li> <li>Kempf Manconi Spalt distraction data</li> </ol> <p> </p> <p> </p> <p><span><strong>All links in our collection</strong></span></p> <p>EDGAR Log Batch 1: <a href="../records/10403941">https://zenodo.org/records/10403941</a></p> <p>EDGAR Log Batch 2: <a href="../records/10404834">https://zenodo.org/records/10404834</a></p> <p>EDGAR Log Batch 3: <a href="../records/10404840">https://zenodo.org/records/10404840</a></p> <p>EDGAR Log Batch 4: <a href="../records/10404846">https://zenodo.org/records/10404846</a></p> <p>EDGAR Log Batch 5: <a href="../records/10404858">https://zenodo.org/records/10404858</a></p> <p>EDGAR Log Batch 6: https://zenodo.org/records/10408818</p> <p>EDGAR Log Batch 7 : <a href="../records/10408820">https://zenodo.org/records/10408820</a></p> <p>Patent data: <a href="../records/10426559">https://zenodo.org/records/10426559</a></p> <p>Miscelleanous data: <a href="../records/10440397">https://zenodo.org/records/10440397</a></p> <p> </p>
Bangla License Plate Dataset 2.5k
<p>This comprehensive dataset of 2519 Bangladeshi vehicle images with clearly legible Bangla license plates. The dataset contains preprocessed license plate images for detection and recognition systems.</p> <p>Upon downloading, you will get four directories:</p> <p>1. training: Contains 2211 high-resolution Bangla license plate images of variable sizes cropped from pictures with license plates. All the files are in jpg format.</p> <p>2. training_data: Contains 2211 Bangla license plate images of 256 x 192 size, resized from images from the training directory. All the files are in png format.</p> <p>3. testing: Contains 200 high-resolution Bangla license plate images of variable sizes cropped from pictures with license plates. All the files are in jpg format.</p> <p>4. training_data: Contains 200 Bangla license plate images of 256 x 192 size, resized from images from the testing directory. All the files are in png format. </p> <p>5. new data: Contains extra 308 images of variable size, </p>
Serious board-game "Licenses to kill" (english version)
<p>Since 2015, the Central Documentation Service (CDS) of the University of French Guiana is engaged in the renewal of their teaching capacities. Some game segments are introduced on a regular basis into its courses in the document methodology in order to promote interactivity and the pleasure of learning.</p> <p>Following the creation of three board games on Open Access in 2017, the university library is introducing “Licenses to Kill”, a new teaching game created in 2018. This version in French language is avaible at this address : <a href="https://zenodo.org/record/1972975#.XQ0b6etKjIU">https://zenodo.org/record/1972975#.XQ0b6etKjIU</a>. The Game Design is made by our Guianese graphic provider Signarama (Cayenne, French Guiana).</p> <p>In 2019, this game was translated into English to be presented at the LIBER European Libraries congress at Dublin and at the IFLA International Libraries Congress at Athens. The traductors were a team of student-translators from L2 LLCER English in Theme and Journalistic Version of the University of French Guiana. Their supervisor teacher was Mr. PINDARD, a professional Traductor for Y.A.N.A Traduction (Cayenne, French Guiana)</p> <p>This game, originally made for Master and Doctorate students, is used in order to teach them difficult-to-learn and sometimes boring skills in a fun and visual way.</p> <p>In the context to the fight against plagiarism, “Licenses to Kill” allows the student to familiarize himself with the major intellectual property licenses (Copyright, Public Property, Creative Commons, and Copyleft) and assimilate the terms of re-use of content that he may use for academic purposes. All the game’s files are freely available and downloadable under the Creative Commons License such as the attribution rights (BY), Non-Commercial rights (NC), and Share-Alike rights (SA) on the long-term archiving website in three different forms: JPEG, PDF and InDesign. They are modifiable and publishable, but it is requested to refrain from commercial use. Institutions interested in buying a copy of the game are offered this possibility: besides the price of the game, a fixed price of €30 ($34) per game must be paid to the University of French Guiana.</p> <p> This amount will be reinvested in the creation of new educational and enjoyable products, which will be shared with the community!</p> <p>Contact : marie.latour@unc.nc</p>
Glutamine sensing licenses cholesterol synthesis.
<p>The uploaded metabolomic dataset contains liquid-chromatography-mass spectrometry (LC-MS) data associated to a publication of Bruna Martins Garcia et al. from the Lena Pernas laboratory.</p> <p>The title olf the article is: <strong>Glutamine sensing licenses cholesterol synthesis.</strong></p> <p>This article is to be published 2024 in the EMBO Journal-</p> <p>Abstract of the article: The mevalonate pathway produces essential metabolites such as cholesterol. Although this pathway is negatively regulated by metabolic intermediates, little is known of the metabolites that positively regulate its activity.<em> </em>We found that the amino acid glutamine is required to activate the mevalonate pathway. Glutamine starvation inhibited cholesterol synthesis and blocked transcription of the mevalonate pathway—even in the presence of glutamine derivatives such as ammonia and a-ketoglutarate. We pinpointed this glutamine-dependent effect to a loss in the ER-to-Golgi trafficking of SCAP that licenses the activation of SREBP2, the major transcriptional regulator of cholesterol synthesis. Both enforced Golgi-to-ER retro-translocation and the expression of a nuclear SREBP2 rescued mevalonate pathway activity during glutamine starvation. In a cell model of impaired mitochondrial respiration in which glutamine uptake is enhanced, SREBP2 activation and cellular cholesterol were increased. Thus, the mevalonate pathway senses and is activated by glutamine at a previously uncharacterized step, and the modulation of glutamine synthesis may be a strategy to regulate cholesterol levels in pathophysiological conditions. </p> <p>The associated data in this repository is grouped according to the figures in the the above mentioned article. Each zip folder contains the LC-MS raw files and one or more Excel tables describing the parameters (retention time, observed molecular weight, detected error to expected molecular weight, signal-to-noise and the integrated raw values of the detected compounds. Material and Method utilized for the analysis of the diverse samples is available in the context of the above mentioned article.</p>
Replication Package for "Ensuring Open Source Integrity: The Intersection of Copy-Based Reuse and License Compliance"
<p>Replication Package for "Ensuring Open Source Integrity: The Intersection of Copy-Based Reuse and License Compliance"<br><br>Includes datasets, R and bash code.</p>
Appendix-C: Text Data and Mining Licensing Conditions
<p>Appendix C is associated with <em><strong>Chapter 11: Text Data and Mining Ethics</strong></em> of the book -- Manika Lamba and Margam Madhusudhan (2021) Text Mining for Information Professionals: An Uncharted Territory, SpringerNature.</p>
WorldCereal open global harmonized reference data repository (CC-BY-NC licensed data sets)
<p>Within the <strong>ESA funded</strong> WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets.</p> <p>This publication includes those harmonized data sets of which the original data set was published under the CC-BY-NC license or a license similar to CC-BY-NC. See document "_In-situ-data-World-Cereal - license - CC-BY-NC.pdf" for an overview of the original data sets. Currently this publication only includes a few small data sets for Tanzania originating from a disease monitoring program of the International Maize and Wheat Improvement Center (CIMMYT). CIMMYT made more data available for countries like Kenya, Ethiopia, Rwanda and, Malawi. However due project contraints these data sets were not yet harmonized.</p>
CLARA Knowledge Graph of licensed educational resources (using RDF-star, Standard reification, Singleton properties, or Named graphs)
<p><strong>CLARA</strong><br>This deposit is part of the <a href="https://project.inria.fr/clara/">CLARA project</a>. The CLARA project aims to empower teachers in the task of creating new educational resources. And in particular with the task of handling the licenses of reused educational resources.</p> <p>The present deposit contains the RDF files created using an RDF mapping (<a href="https://rml.io/">RML</a>) and a mapper (<a href="https://github.com/morph-kgc/morph-kgc">Morph-KGC</a>). It also contains the files JSON used as input. The corresponding pipeline can be found on <a href="https://gitlab.univ-nantes.fr/clara/pipeline">Gitlab</a>. The data used in that pipeline originate from <a href="https://www.x5gon.org/">X5GON</a>, a European project aiming to generate and gather open educational resources.</p> <p><strong>Knowledge graph content</strong><br>The present Knowledge Graph contains information about 45K Educational Resources (ERs) and 135K subjects (extracted from DBpedia).<br>That information contains </p> <ul> <li>the author,</li> <li>its title and description</li> <li>the license,</li> <li>a URL to the resource itself,</li> <li>the language of the ER,</li> <li>its mimetype,</li> <li>and finally which subject it talks about, and to what extent.</li> </ul> <p><br>That extent is given by two scores: a PageRank score and a Cosinus score.</p> <p>A particularity of the knowledge graph is its heavy use of RDF reification, across large multi-valued properties.<br>Thus four versions of the knowledge graph exist, using Standard reification, Singleton property, Named graphs, and RDF-star.</p> <p>The Knowledge Graph also contains <a href="https://databus.dbpedia.org/dbpedia/generic/categories">categories</a> originating from DBpedia. They help precise the subjects that are also extracted from DBpedia.</p> <p>The KG.zip files contain five types of files:</p> <ul> <li><strong>Authors_[</strong>X<strong>].nt</strong> - Those contain the authors' nodes, their type, and name.</li> <li><strong>ER_[</strong>X<strong>].nt/nq/ttl</strong> - Those contain the ERs and their information using the respective RDF reification model.</li> <li><strong>categories_skos_[</strong>X<strong>].ttl</strong> - Those contain the hierarchy of DBpedia categories.</li> <li><strong>categories_labels.ttl </strong>- This file contains additional information about the categories.</li> <li><strong>categories_article.ttl</strong> - This file contains the RDF triples that link the DBpedia subjects to the DBpedia categories.</li> </ul> <p> </p> <p><strong>JSON content</strong></p> <p>The original dataset was cut into multiple JSON files in order to make its processing easier. DBpedia categories were extracted as RDF and aren't present in the JSON files.<br><br>There are two types of files in the input-json.zip file:</p> <ul> <li><strong>authors_[</strong>X<strong>].json</strong> - Which lists the authors names</li> <li><strong>ER_[</strong>X<strong>].json</strong> - Which lists the ERs and their related information.<br>That information contains: <ul> <li>their <em>title.</em></li> <li>their <em>description.</em></li> <li>their <em>language</em> (and <em>language_detected</em>, only the first one is used in the pipeline here).</li> <li>their <em>license.</em></li> <li>their <em>mimetype.</em></li> <li>the <em>authors.</em></li> <li>the <em>date</em> of creation of the resource.</li> <li>a <em>url</em> linking to the resource itself.</li> <li>the subjects (named <em>concepts</em>) associated with the resource. With the corresponding scores.</li> </ul> </li> </ul> <p> </p> <p>If you do use this dataset, you can cite the corresponding paper:</p> <ul> <li>Kieffer, M., Fakih, G. & Serrano-Alvarado, P. (2023). Evaluating Reification with Multi-valued Properties in a Knowledge Graph of Licensed Educational Resources. Semantics, Leipzig, Germany.</li> </ul>
Safety of Hib-MenCY-TT Vaccine Versus Licensed Hib Conjugate Vaccine, Given at 12 to 15 Months of Age.
ClinicalTrials.gov study NCT00345683. IPD Sharing: YES. Countries: 2. Publications: 1.
Comparison of GSK Biologicals' Meningococcal Vaccine (GSK134612) and Licensed MenC-CRM197 Vaccine in Healthy Children
ClinicalTrials.gov study NCT00674583. IPD Sharing: YES. Countries: 2. Publications: 1.
Clinical Study in Children, 6 Months to 3 Years of Age, to Assess Two Dose Levels of an Experimental Flu Vaccine, Using a Licensed Influenza Virus Vaccine, Vaxigrip® as the Control
ClinicalTrials.gov study NCT00778895. IPD Sharing: YES. Countries: 1. Publications: 1.
Safety of Hib-MenCY-TT Vaccine Versus Licensed Hib Conjugate Vaccine, Given at 2, 4, 6 and 12 to 15 Months of Age
ClinicalTrials.gov study NCT00345579. IPD Sharing: YES. Countries: 2. Publications: 2.
A Study to Compare the Immunogenicity of GSK Biologicals' 10Pn-PD-DiT 4-dose Presentation to the Licensed 1-dose Synflorix™ (10Pn-PD-DiT) Vaccine When Co-administered With DTPw-combination Vaccine in
ClinicalTrials.gov study NCT02447432. IPD Sharing: YES. Countries: 1. Publications: 1.
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.