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7,515
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Dataset results
7,515 results for “screenings”
Alcohol Screening and Pre-Operative Intervention Research Study
ClinicalTrials.gov study NCT03929562. IPD Sharing: YES. Countries: 1. Publications: 1.
Self-Testing Options in the Era of Primary HPV Screening for Cervical Cancer Trial
ClinicalTrials.gov study NCT04679675. IPD Sharing: YES. Countries: 1. Publications: 2.
Integrating a Stepped Care Model of Screening and Treatment for Depression Into Malawi's National HIV Care Delivery Platform
ClinicalTrials.gov study NCT04777006. IPD Sharing: YES. Countries: 1. Publications: 11.
Promoting Informed Decisions About Cancer Screening in Older Adults
ClinicalTrials.gov study NCT03959696. IPD Sharing: YES. Countries: 1. Publications: 2.
Prospective Evaluation of Self-Testing to Increase Screening
ClinicalTrials.gov study NCT03898167. IPD Sharing: YES. Countries: 1. Publications: 5.
Validation of the STarT Back Screening Tool in the Military
ClinicalTrials.gov study NCT03127826. IPD Sharing: YES. Countries: 1. Publications: 6.
Helping Patients and Providers Make Better Decisions About Colorectal Cancer Screening
ClinicalTrials.gov study NCT04683731. IPD Sharing: YES. Countries: 1. Publications: 1.
Paired Promotion of Colorectal Cancer and Social Determinants of Health Screening
ClinicalTrials.gov study NCT04585919. IPD Sharing: YES. Countries: 1. Publications: 2.
Abbreviated Breast MRI and Digital Tomosynthesis Mammography in Screening Women With Dense Breasts
ClinicalTrials.gov study NCT02933489. IPD Sharing: YES. Countries: 2. Publications: 1.
Assessing consistency across functional screening datasets in cancer cells
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Data from: Predicting success of conservation translocations: Prerelease screening tools for a threatened marsupial
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Data from: Screening familial risk for hereditary breast and ovarian cancer
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BARCODE: high throughput screening and analysis of soft active materials
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Feasibility and acceptability of personalized breast cancer screening (DECIDO Study): A single-arm proof-of-concept trial
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Data from: DNA metabarcoding for biodiversity monitoring in a national park: screening for invasive and pest species
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Nationwide real-world implementation of AI for cancer detection in population-based mammography screening (PRAIM)
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Development of a high-throughput small molecule screening assay for phenotypical characterization of lysosomal storage disorder-affected cells, with infantile cystinosis as a proof of principle
<p>Together with the Pivot Park Screening Centre we performed a drug screen on CTNS-/- proximal tubule cells. For this we developed an assay to evaluate LC3-II positive puncta, and which may be applied for any disease in which autophagy plays an important role. The screen was optimized by the hotel for a 384 well format, making it useful for high throughput screening. The screen was performed with 1280 compounds from the Prestwick library.</p>
Yeast 1-hybrid screens for upstream regulators of A. thaliana AGO1, AGO7, and AGO10: raw data and R code
<p>These files document a yeast 1-hybrid experiments and associated analyses described in a paper by Hoyer et al. (2019): <a href="https://doi.org/10.1002/pld3.102">https://doi.org/10.1002/pld3.102</a></p> <p>This release corresponds to the fourth version of the Zenodo record. None of the code or data files changed from record 1472704 (the version linked in the paper); I simply added a link to the <a href="https://doi.org/10.1002/pld3.102">Plant Direct paper</a> to the ReadMe file.</p>
Supplementary Material for the paper: Automatic Document Screening of Medical Literature Using Word and Text Embeddings in an Active Learning Setting
<p>This is the dataset used in the paper: Automatic Document Screening of Medical Literature Using Word and Text Embeddings in an Active Learning Setting. </p> <p>It is composed of: </p> <p>- Pre-trained models using active learning for document screening on HealthCLEF and Epistemonikos datasets. </p> <p>- Epistemonikos and HealthCLEF datasets containing medical questions and relevant/non relevant articles. </p> <p>- Embeddings and Document Representations used for experiments on both datasets. </p> <p>Scripts to run experiments can be found at: <a href="https://github.com/afcarvallo/active_learning_document_screening">https://github.com/afcarvallo/active_learning_document_screening</a></p> <p> </p> <p><strong>Paper abstract:</strong></p> <p>Document screening is a fundamental task within Evidence-based Medicine (EBM), a practice that provides scientific evidence to support medical decisions. Several approaches have tried to reduce physicians' workload of screening and labeling vast amounts of documents to answer clinical questions. Previous works tried to semi-automate document screening, reporting promising results, but their evaluation was conducted on small datasets, which hinders generalization. Moreover, recent works in natural language processing have introduced neural language models, but none have compared their performance in EBM. In this paper, we evaluate the impact of several document representations such as TF-IDF along with neural language models (BioBERT, BERT, Word2vec, and GloVe) on an active learning-based setting for document screening in EBM. Our goal is to reduce the number of documents that physicians need to label to answer clinical questions. We evaluate these methods using both a small challenging dataset (HealthCLEF 2017) as well as a larger one but easier to rank (Epistemonikos). Our results indicate that word as well as textual neural embeddings always outperform the traditional TF-IDF representation. When comparing among neural and textual embeddings, in the HealthCLEF dataset the models BERT and BioBERT yielded the best results. On the larger dataset, Epistemonikos, Word2Vec and BERT were the most competitive, showing that BERT was the most consistent model across different corpuses. In term of active learning, an uncertainty sampling strategy combined with logistic regression achieved the best performance overall, above other methods under evaluation, and in fewer iterations.</p>
Data from: Genome-wide Screens Implicate Loss of Cullin Ring Ligase 3 in Persistent Proliferation and Genome Instability in TP53-Deficient Cells
<p>CSV files of whole-Genome Knockout Screens for Proliferation and Tumorigenic Growth. The data is retrieved from:</p> <p>Title: "Genome-wide Screens Implicate Loss of Cullin Ring Ligase 3 in Persistent Proliferation and Genome Instability in TP53-Deficient Cells"</p> <p>DOI: https://doi.org/10.1016/j.celrep.2020.03.029</p> <p>The excel sheet with data shown in figure 1B is converted to CSV files.</p>
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.