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32 results for “biomedical dataset”
Pathway2Text: Dataset for Biomedical Pathway Description Generation
<p>This is the dataset of the NAACL 2022 paper:</p> <p>Pathway2Text: Dataset and Method for Biomedical Pathway Description Generation.</p> <p>This dataset contains 2,367 pairs of biomedical pathways and textual descriptions. It can be used for automatic pathway description generation. In our paper, we showed it is also appropriate for Text2Graph and BioNER.</p> <p>Read readme.pdf for detaild information.</p>
NED data for the paper Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts
<p>This data repository contains NED data from the paper, <em><a href="https://arxiv.org/abs/2309.01812">Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts.</a></em></p> <p>Additional data for the NER classification task can be found here: <a href="../records/10050681">zenodo</a></p> <p> </p> <p> </p>
Dataset for Engineering multifunctional dynamic hydrogel for biomedical and tissue regenerative applications
<p><span>Hydrogels have emerged in various biomedical applications, including tissue engineering and medical devices, due to their ability to imitate the natural extracellular matrix (ECM) of tissues. However, conventional static hydrogels lack the ability to dynamically respond to changes in their surroundings to withstand the robust changes of the biophysical microenvironment and to trigger on-demand functionality such as drug release and mechanical change. In contrast, multifunctional dynamic hydrogels can adapt and respond to external stimuli and have drawn great attention in recent studies. It is realized that the integration of nanomaterials into dynamic hydrogels provides numerous functionalities for a great variety of biomedical applications that cannot be achieved by conventional hydrogels. This review article provides a comprehensive overview of recent advances in designing and fabricating dynamic hydrogels for biomedical applications. We describe different types of dynamic hydrogels based on breakable and reversible covalent bonds as well as noncovalent interactions. These mechanisms are described in detail as a useful reference for designing crosslinking strategies that strongly influence the mechanical properties of the hydrogels. We also discuss the use of dynamic hydrogels and their potential benefits. This review further explores different biomedical applications of dynamic nanocomposite hydrogels, including their use in drug delivery, tissue engineering, bioadhesives, wound healing, cancer treatment, and mechanistic study, as well as multiple-scale biomedical applications. Finally, we discuss the challenges and future perspectives of dynamic hydrogels in the field of biomedical engineering, including the integration of diverse technologies.</span></p>
Dataset for the article "Biomedical Publishing in Russia: How big is low-quality papers problem?"
<p>The dataset is used in the article "Biomedical Publishing in Russia: How big is low-quality papers problem?" submitted to the Journal of the Medical Library Association : JMLA.</p> <p><br>The dataset contains the list of Russian and international biomedical journals along with their ISSNs, and eISSNs with attached thematic categories from Web of Science and SJR. For each journal-year pair, the number of publications is calculated. The time frame of analysis is 2010-2020.</p>
WMT'16 Biomedical Translation Task - Scielo monolingual datasets
<p>Monolingual data from Scielo for the Biomedical Translation Task in the First Conference on Machine Translation (WMT 16) (http://www.statmt.org/wmt16/biomedical-translation-task.html).</p> <p>It contains monolingual data for en, es, pt and fr.</p> <p>The documents were derived from the Scielo database (https://scielo.org/en/).</p>
WMT'16 Biomedical Translation Task - Scielo parallel datasets
<p>Parallel data from Scielo for the Biomedical Translation Task in the First Conference on Machine Translation (WMT 16) (http://www.statmt.org/wmt16/biomedical-translation-task.html).</p> <p>It contains parallel data for es/en, fr/en and pt/en.</p> <p>The documents were derived from the Scielo database (https://scielo.org/en/).</p>
Multimodal Biomedical Dataset for Evaluating Registration Methods (patches from TMA Cores)
<p>The dataset consists of 206 aligned Bright-Field (BF) and Second-Harmonic Generation (SHG) images. All images are of the same size of 834x834 pixels. The training set contains 40 image pairs. A validation set 1 of 25 image pairs for tuning the hyperparameters for the training. A validation set 2 of 7 image pairs for tuning the registration method. The test set for evaluation: 134 image pairs (times two).</p> <p>For each image pair in the test set, a random rotation up to +/-30 degrees and random translations in x and y for up to 100px were applied. For each image pair that was transformed for the test set, we apply the transformation to both modalities and keep their corresponding reference images as well as transformed images, in order to allow for registration using each modality as a reference and the other as a floating image. The metadata.csv file includes the coordinates of the four corners of the reference image as well as the coordinates of the corners of the transformed image after displacement. The origin of this coordinate system is the upper left corner of the reference image, given by (0,0).</p> <p>Example of naming convention in the test set:</p> <ul> <li>R_1B_F8_BF.tif: reference image in Bright-Field</li> <li>R_1B_F8_SHG.tif: reference image in Second-Harmonic Generation</li> <li>T_1B_F8_BF.tif: transformed image in Bright-Field</li> <li>T_1B_F8_SHG.tif: transformed image in Second-Harmonic Generation.</li> </ul> <p>Where the transformations applied to T_1B_F8_BF.tif and T_1B_F8_SHG.tif are identical and hence the coordinates of the corners in metadata.csv are valid for both image pairs.</p> <p>The metadata.csv file contains the following columns:</p> <ul> <li>Filename: identifier for image pair</li> <li>X1_Ref: x-coordinate of upper left corner of reference image</li> <li>Y1_Ref: y-coordinate of upper left corner of reference image</li> <li>X2_Ref: x-coordinate of lower left corner of reference image</li> <li>Y2_Ref: y-coordinate of lower left corner of reference image</li> <li>X3_Ref: x-coordinate of upper right corner of reference image</li> <li>Y3_Ref: y-coordinate of upper right corner of reference image</li> <li>X4_Ref: x-coordinate of lower right corner of reference image</li> <li>Y4_Ref: y-coordinate of lower right corner of reference image</li> <li>X1_Trans: x-coordinate of upper left corner of transformed image</li> <li>Y1_Trans: y-coordinate of upper left corner of transformed image</li> <li>X2_Trans: x-coordinate of lower left corner of transformed image</li> <li>Y2_Trans: y-coordinate of lower left corner of transformed image</li> <li>X3_Trans: x-coordinate of upper right corner of transformed image</li> <li>Y3_Trans: y-coordinate of upper right corner of transformed image</li> <li>X4_Trans: x-coordinate of lower right corner of transformed image</li> <li>Y4_Trans: y-coordinate of lower right corner of transformed image</li> <li>Displacement: Mean Euclidean distance between reference corner points and transformed corner points</li> </ul> <p>The data set was originally produced by the authors of <em>Aligned Collagen Is a Prognostic Signature for Survival in Human Breast Carcinoma</em> (<a href="https://www.sciencedirect.com/science/article/pii/S0002944010002336">https://www.sciencedirect.com/science/article/pii/S0002944010002336</a>). The registered and non-registered sub-image pairs in this data set were created by Johan Öfverstedt and Elisabeth Wetzer.</p>
Benchmark datasets for biomedical knowledge graphs with negative statements
<p>We present a collection of datasets for three relation prediction tasks - protein-protein interaction prediction, gene-disease association prediction and disease prediction - that aim at circumventing the difficulties in building benchmarks for knowledge graphs with negative statements. These datasets include data from two successful biomedical ontologies, Gene Ontology and Human Phenotype Ontology, enriched with negative statements. </p>
WMT'16 Biomedical Translation Task - Scielo parallel datasets - GMA alignment files
<p>Aligment files using the GMA tool (https://nlp.cs.nyu.edu/GMA/) for the parallel data from Scielo for the Biomedical Translation Task in the First Conference on Machine Translation (WMT 16) (http://www.statmt.org/wmt16/biomedical-translation-task.html).</p> <p>The parallel data is available here: https://zenodo.org/record/5588265</p>
Dataset: FAIR Biomedical Research Software (FAIR-BioRS) manuscript
<p>Data related to our FAIR-BioRS manuscript. More details are available at the associated GitHub repository: <a href="https://github.com/FAIR-BioRS/Data">https://github.com/FAIR-BioRS/Data</a>. </p>
WMT'16 Biomedical Translation Task - Medline parallel datasets
<p>Parallel data from Medline for the Biomedical Translation Task in the First Conference on Machine Translation (WMT 16) (http://www.statmt.org/wmt16/biomedical-translation-task.html).</p> <p>It contains parallel data for es/en, fr/en and pt/en.</p> <p>The documents were derived from the Medline/PubMed database (https://pubmed.ncbi.nlm.nih.gov/).</p>
Dataset: Relationship extraction for knowledge graph creation from biomedical literature (Gene-Disease relationships)
<p>This is the dataset used for classifying Gene-Disease relationship types from sentences. The dataset consists of 3 files:</p> <ul> <li>manually_annotated_set.xlsx - set of 2000 manualy annotated sentences with entities</li> <li>Unbalanced_dataset.xlsx - set of 12000 sentences, out of which 2000 are from the first set, manually annotated, and the rest have been added using rule based method by adding sentences where extraction had confidence 1.</li> <li>Balanced_dataset_SUB_PRED.xlsx - balanced dataset generated by taking 2000 manually annotated sentences, but then adding sentences from the rule-based method with confidence 1 in such a way that each relationship class had at least 1400 sentences (for biomarkers, we could obtain 1243 sentences with confidence 1 from a processed portion of the data we had at the time of building the dataset).</li> </ul> <p> </p>
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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)
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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.