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2,129 results for “Score”
Microglia transcriptome systematic scoring to evaluate the efficacy of human cord derived mesenchymal stem cells treatment protocols in a rat model of inflammation associated preterm infant brain inju
GEO Series GSE298271. Rattus norvegicus. 90 samples. Type: Expression profiling by high throughput sequencing.
DNA Repair Function Scores for 2172 Variants in the BRCA1 Amino-Terminus
GEO Series GSE234482. Homo sapiens. 16 samples. Type: Other.
Lichenoid chronic graft-versus-host skin displays high interferon score with promising effect of IFNAR1 inhibitor on local inflammation
GEO Series GSE277089. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
A p53 score derived from TP53 CRISPR/Cas9 HMCLs predicts survival and reveals major role of BAX in BH3 mimetics response [2]
GEO Series GSE245162. Homo sapiens. 96 samples. Type: Expression profiling by high throughput sequencing.
An 18 gene expression-based score classifier predicts the clinical outcome in stage 4 neuroblastoma
GEO Series GSE79910. Homo sapiens. 27 samples. Type: Expression profiling by array.
Gene signatures in prostate cancer linked to the Gleason score
GEO Series GSE32571. Homo sapiens. 98 samples. Type: Expression profiling by array.
Beware of the generic machine learning-based scoring functions in structure-based virtual screening
<p>Data sets and the rescoing scores utilized in the paper "Beware of the generic machine learning-based scoring functions in structure-based virtual screening" (DOI: 10.1093/bib/bbaa070)</p>
Accuracy or novelty: what can we gain from target-specific machine learning-based scoring functions in virtual screening?
<p>Datasets, features, and some representative scripts utilized in the paper "Accuracy or novelty: what can we gain from target-specific machine learning-based scoring functions in virtual screening?" </p>
Data from: A methylation-to-expression feature model for generating accurate prognostic risk scores and identifying disease targets in clear cell kidney cancer
Many researchers now have available multiple high-dimensional molecular and clinical datasets when studying a disease. As we enter this multi-omic era of data analysis, new approaches that combine different levels of data (e.g. at the genomic and epigenomic levels) are required to fully capitalize on this opportunity. In this work, we outline a new approach to multi-omic data integration, which combines molecular and clinical predictors as part of a single analysis to create a prognostic risk score for clear cell renal cell carcinoma. The approach integrates data in multiple ways and yet creates models that are relatively straightforward to interpret and with a high level of performance. Furthermore, the proposed process of data integration itself captures relationships in the data that represent highly disease-relevant functions.
Data from: Harnessing olfactory bulb oscillations to perform fully brain-based sleep-scoring and real-time monitoring of anesthesia depth
Real-time tracking of vigilance states related to both sleep or anaesthesia has been a goal for over a century. However, sleep scoring cannot currently be performed with brain signals alone, despite the deep neuromodulatory transformations that accompany sleep state changes. Therefore, at heart, the operational distinction between sleep and wake is that of immobility and movement, despite numerous situations in which this one-to-one mapping fails. Here we demonstrate, using local field potential (LFP) recordings in freely moving mice, that gamma (50–70 Hz) power in the olfactory bulb (OB) allows for clear classification of sleep and wake, thus providing a brain-based criterion to distinguish these two vigilance states without relying on motor activity. Coupled with hippocampal theta activity, it allows the elaboration of a sleep scoring algorithm that relies on brain activity alone. This method reaches over 90% homology with classical methods based on muscular activity (electromyography [EMG]) and video tracking. Moreover, contrary to EMG, OB gamma power allows correct discrimination between sleep and immobility in ambiguous situations such as fear-related freezing. We use the instantaneous power of hippocampal theta oscillation and OB gamma oscillation to construct a 2D phase space that is highly robust throughout time, across individual mice and mouse strains, and under classical drug treatment. Dynamic analysis of trajectories within this space yields a novel characterisation of sleep/wake transitions: whereas waking up is a fast and direct transition that can be modelled by a ballistic trajectory, falling asleep is best described as a stochastic and gradual state change. Finally, we demonstrate that OB oscillations also allow us to track other vigilance states. Non-REM (NREM) and rapid eye movement (REM) sleep can be distinguished with high accuracy based on beta (10–15 Hz) power. More importantly, we show that depth of anaesthesia can be tracked in real time using OB gamma power. Indeed, the gamma power predicts and anticipates the motor response to stimulation both in the steady state under constant anaesthetic and dynamically during the recovery period. Altogether, this methodology opens the avenue for multi-timescale characterisation of brain states and provides an unprecedented window onto levels of vigilance.
Scores for indicators of IKS inclusivity applied to DFO advice documents published during 2019-2024
<p>This data set is the basis for our paper, <em>Inclusivity of Indigenous Knowledge Systems in Fisheries Management</em>, first submitted to <em>Fish and Fisheries</em> on November 24, 2024, revision invited on January 30, 2025, and re-submission on February 2025..</p> <p>Data are presented in a spreadsheet with three tables.</p> <p><strong>The first table</strong> reproduces information from Table 2 in the manuscript, describing each indicator and its scoring criteria. </p> <p><strong>The second table</strong> contains information on each of the advice documents analyzed, including their full citation hyperlinked to the original document. While each column is self-explanatory, we highlight the following:</p> <ul> <li><strong>Column H</strong> provides scores for each indicator that applies to the document, and <strong>Column J</strong> normalizes the scores. </li> <li><strong>Column J</strong> provides the justification for each score. </li> <li> <strong>Column K</strong> provides, in a few cases, additional information which does not necessarily contribute to the score's justification but which provides additional context. </li> </ul> <p><strong>The third table</strong> defines acronyms.</p>
Dataset Of Diagnosing Medical Score Calculator Apps Paper
<p>Dataset Of Diagnosing Medical Score Calculator Apps Paper</p>
Comet assay annotation data for comet segmentation and scoring (1037 comet assay images)
<p>Comet assay annotation images used in the following paper</p> <p>Hong, Y., Han, HJ., Lee, H. <em>et al.</em> Deep learning method for comet segmentation and comet assay image analysis. <em>Sci Rep</em> <strong>10, </strong>18915 (2020). https://doi.org/10.1038/s41598-020-75592-7</p> <p>DeepComet online service website: <strong>https://ad3.io/</strong></p> <p>sign up->dashboard->APPs->Deep Comets->new job</p> <p> </p> <p>Two biology researchers who were experienced in the comet assay for over half a year under the guidance of a toxicology expert in the assay annotated the images independently and compared each other’s work to ensure consistency. Here, the <strong>VGG Image Annotator sofware</strong> was used to annotate the images manually. We annotated the intact comet heads without a tail using a circular region tool that outputs the center and radius of the circle. Further, comets with a tail and DNA damage were annotated with a polyline tool, where the frst dot was marked at the center of a comet head, and the second dot was marked at the bottom of the comet head. Tese two points were used to identify the head of a comet. Subsequently, dots were marked along the boundary of the comet tail counterclockwise from the second dot. Te total number of dots were at least eight per each comet.</p> <p>Comets can be sorted into non-ghost cells with a distinct head and tail, and ghost cells with a small or no nucleoid head and a broad tail. The ghost cells are usually classifed as the highest degree of DNA damage by visual scoring. Until now, there is a controversy over the cause of ghost cells and how they should be analyzed appropriately. Therefore, our method is designed to classify these two kinds of cells, so one can use some custom analysis techniques for ghost cells.</p> <p>The comets in our datasets were all classifed as non-ghost or ghost cells. Furthermore, we assigned tags to the comets if they were overlapped or located at the boundary of an image (i.e., only a part of the comet is visible). After manual annotation with dots and tags (in the red box), each dotted contour was flled up to give mask images.</p>
Supplement Material for "Nutritional assessment of ready-to-eat salads in supermarkets: comparison of the nutriRECIPE-Index and the Nutri-Score"
<p>Supplement Material for "Nutritional assessment of ready-to-eat salads in supermarkets: comparison of the nutriRECIPE-Index and the Nutri-Score"</p>
Interval-based Verification for Medical Scores and Score Calculator Apps
<p>Dataset Of Interval-based Verification for Medical Scores and Score Calculator Apps</p>
Q Score Segmented FAST5 Evaluation Data Set
<p>Q Score segmented raw FAST5 data set used for accuracy characterization of the novel Alignment Matrix soft decoding algorithm (<a href="https://doi.org/10.5281/zenodo.11454877">https://doi.org/10.5281/zenodo.11454877</a>) applied to the HEDGES DNA-information storage code. Implementation of the HEDGES code used for accuracy assessment of our algorithm is based on the publication of Press et al. (<a href="https://doi.org/10.1073/pnas.2004821117">https://doi.org/10.1073/pnas.2004821117</a>).</p> <p><strong> </strong></p> <p>Each archive in this data set generally corresponds to a certain design length and HEDGES rate. For example, 1250, 1667, 3333, and 5000 correspond to HEDGES rates of 0.125, 0.167, 0.33, and 0.5 respectively. Additionally, archives labeled with "half" and "quarter" indicate DNA molecule designs that are approximately half and quarter the length of archives labeled "full". Archives labeled with "s1" or "s2" correpsond to data for strands indexed as 1 and 2 for the 0.167 hedges "full" design. Within each archive are FAST5 directories that each correspond to Q Score segment ranges that were used to evaluate the impact of Q Score on soft decoding byte error rate. Each FAST5 directory is clearly labeled with the start and end Q Score value that was used to construct the data set. </p> <p> </p>
Pre-test and post-test scores on Grammar and Self Directed Learning in Vietnamese French class
<p>The results from 5-scale Likert survey for sefl-directed learning and grammar test before and after introducing ChatGPT as a learning instrument in a French class in University of Transport and Communications, Hanoi, Vietnam. </p> <p><strong>SURVEY FORM 01 - QUESTIONS</strong></p> <p>1<span> </span>I am satisfied with my university life overall</p> <p>2<span> </span>I am satisfied with my chosen major.</p> <p>3<span> </span>I am interested in the French courses.</p> <p>4<span> </span>I feel confident and in control when using digital technologies.</p> <p>5<span> </span>I frequently engage with ChatGPT for various tasks.</p> <p>6<span> </span>I know what I need to learn. </p> <p>7<span> </span>Regardless of the result or effectiveness of my learning, I still like learning. </p> <p>8<span> </span>I strongly hope to constantly improve and excel in my learning. </p> <p>9<span> </span>My successes and failures inspire me to continue learning. </p> <p>10<span> </span>I enjoy finding answers to questions.</p> <p>11<span> </span>I will not give up learning because I face some difficulties. </p> <p>12<span> </span>I can proactively establish my learning goals. </p> <p>13<span> </span>I know what learning strategies are appropriate for me in reaching my learning goals. </p> <p>14<span> </span>I set the priorities of my learning.</p> <p>15<span> </span>In the classroom or on my own, I am able to follow my own plan of learning.</p> <p>16<span> </span>I am good at arranging and controlling my learning time.</p> <p>17<span> </span>I know how to find resources for my learning.</p> <p>18<span> </span>I can connect new knowledge with my own personal experiences.</p> <p>19<span> </span>I understand the strengths and weakness of my learning.</p> <p>20<span> </span>I can monitor my learning progress.</p> <p>21<span> </span>I can evaluate on my own learning outcomes.</p> <p>22<span> </span>My interaction with others helps me plan for further learning.</p> <p>23<span> </span>I would like to learn the language and culture of those whom I frequently interact with.</p> <p>24<span> </span>I am able to express messages effectively in oral presentations.</p> <p>25<span> </span>I am able to communicate messages effectively in writing.</p> <p><strong>SURVEY FORM 02 - QUESTIONS</strong></p> <p>1<span> </span>In the past five weeks, I have frequently used ChatGPT for learning French grammar.</p> <p>2<span> </span>I find my experience with ChatGPT better compared to traditional grammar learning methods such as textbooks, lectures, or other educational materials.</p> <p>3<span> </span>I am satisfied with the effectiveness of using ChatGPT in learning French grammar.</p> <p>4<span> </span>I feel motivated when using ChatGPT for learning French grammar.</p> <p>5<span> </span>ChatGPT helps me to be more proactive in my study plan.</p> <p>6<span> </span>I am willing to continue using ChatGPT for learning French grammar.</p>
SCORE D3.9 - Models for the long-term evolution of the coastline (package)
<p>SCORE project TD3.9 datasets and scripts:</p> <p>Modelling tools and local datasets produced in the SCORE Task 3.5, for the long-term evolution of the coastline and uncertainties, estimated by the study of local morphodynamic processes and climatic projections. It contains a hybrid 2D/3D modelling chain, for sediment transport processes connected to hydrodynamic models at the storm scale. </p>
Listening Samples for Paper "Score to Audio: Integrating Systems to Transform Music Scores into Expressive Piano Audio"
<p>Listening Samples for Paper "Score to Audio: Integrating Systems to Transform Music Scores into Expressive Piano Audio".</p> <p>Please check the README.md for more details.</p>
Score matching for differential abundance testing of compositional high-throughput sequencing data - data repository
<p>Data repository for "Score matching for differential abundance testing<br>of compositional high-throughput sequencing data" (<a href="https://github.com/bio-datascience/cosmoDA">github</a>)</p> <p>To use, clone the repository, then download the zip file and unpack it in the main directory of the repository.</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.