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2,129 results for “scores”
MACIE scores for human genome assembly GRCh37 Part 4 (Chr14 - Chr22)
<p>MACIE (Multi-dimensional Annotation Class Integrative Estimation) is an unsupervised multivariate mixed model framework to assess multi-dimensional functional impacts for both coding and non-coding variants in the human genome. MACIE integrates a variety of functional annotations, including protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics, and estimates the joint posterior probabilities of each genetic variant being functional.</p> <p>For each non-coding and synonymous coding variant, the MACIE score is a vector of length 4, representing the estimated joint posterior probabilities of “not evolutionarily conserved and regulatory functional” (MACIE01); “evolutionarily conserved and not regulatory functional” (MACIE10); “not evolutionarily conserved and not regulatory functional” (MACIE00); “both evolutionarily conserved and regulatory functional (MACIE11). MACIE_conserved is the estimated posterior probability of “evolutionarily conserved”, which is the sum of MACIE10 and MACIE11; MACIE_regulatory is the estimated posterior probability of “regulatory functional”, which is the sum of MACIE01 and MACIE11; MACIE_anyclass is the estimated posterior probability of “evolutionarily conserved” or “regulatory functional”, which is the sum of MACIE01, MACIE10, and MACIE11.</p>
MACIE scores for human genome assembly GRCh37 Part 3 (Chr8 - Chr13)
<p>MACIE (Multi-dimensional Annotation Class Integrative Estimation) is an unsupervised multivariate mixed model framework to assess multi-dimensional functional impacts for both coding and non-coding variants in the human genome. MACIE integrates a variety of functional annotations, including protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics, and estimates the joint posterior probabilities of each genetic variant being functional.</p> <p>For each non-coding and synonymous coding variant, the MACIE score is a vector of length 4, representing the estimated joint posterior probabilities of “not evolutionarily conserved and regulatory functional” (MACIE01); “evolutionarily conserved and not regulatory functional” (MACIE10); “not evolutionarily conserved and not regulatory functional” (MACIE00); “both evolutionarily conserved and regulatory functional (MACIE11). MACIE_conserved is the estimated posterior probability of “evolutionarily conserved”, which is the sum of MACIE10 and MACIE11; MACIE_regulatory is the estimated posterior probability of “regulatory functional”, which is the sum of MACIE01 and MACIE11; MACIE_anyclass is the estimated posterior probability of “evolutionarily conserved” or “regulatory functional”, which is the sum of MACIE01, MACIE10, and MACIE11.</p>
Data for ESG Score and Green Stock Overnight Returns
<p>The file <code>fivefactor_yearly</code> contains the Fama-French five factors .</p> <p>The file <code>yearly_indicator</code> contains the sample of stocks used in this study.</p>
MODYS-video: 2D Human pose estimation data and Dyskinesia Impairment Scale scores from children and young adults with dyskinetic cerebral palsy
<p>The dataset contains the 2D coordinates in pixels of body landmarks (wrists, ankles, shoulders, hips, knees and ankles) extracted from 188 videos of 34 children with dyskinetic cerebral palsy using DeepLabCut [1] and appertaining clinical scores of the Dyskinesia Impairment Scale (DIS) [2].</p> <p>The videos were collected during the item “lying in rest” and “sitting in rest” of the DIS at three time points during a clinical trial on the effect of intrathecal baclofen [3]. Children had a mean age of 14y2m (SD 4.0), 26 were male. Their gross motor function classification system level ranged from IV-V and their manual ability classification system level from III-V. Original videos have length of 4-35 seconds with a resolution of 720x575 pixels and are sampled with 25 Hz. We added stick figures to complement the data for context and ease of understanding. They were created from the 2D coordinates that were extracted with a likelihood >0.8.</p> <p>Clinical scoring was performed by three trained experts (according to the DIS) on the original videos. Within the items “lying in rest” and “sitting in rest” the amplitude and duration of dystonia and choreoathetosis of the trunk, proximal right arm, proximal left arm, proximal right leg and proximal left leg are scored on a 0-4 ordinal scale and calculated towards a percentage score between 0-1.</p> <p>The dataset can be used in a machine learning approach to automatically assess dystonia and choreoathetosis of children with dyskinetic cerebral palsy using 2D coordinates of body points extracted from videos.</p> <p> </p> <p>References:</p> <p>1. Mathis, A., et al., DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat Neurosci, 2018. 21(9): p. 1281-1289.</p> <p>2. Monbaliu, E., et al., The dyskinesia Impairment Scale: a new instrument to measure dystonia and choreoathetosis in dyskinetic cerebral palsy. Dev Med Child Neurol, 2012. 54: p. 278-283.</p> <p>3. Bonouvrie, L.A., et al., The Effect of Intrathecal Baclofen in Dyskinetic Cerebral Palsy: The IDYS Trial. Ann Neurol, 2019. 86: p. 79-90.</p> <p> </p> <p> </p>
Predictive scoring for risk of complications in pediatric dengue infection
<p class="MsoNormal"><strong><span>Background: </span></strong><span>Dengue infection has been a worrisome cause of mortality and morbidity in children. Though numerous scoring systems have been developed, they are in the adult population or are too complicated for use in children. Pediatric dengue infection has a wide spectrum from a mild illness to severe complications and an unpredictable course. Hence the need for a predictive scoring system where the possibility of complications can be identified which can contribute to reduction in mortality and morbidity of dengue by prompt referrals and anticipatory management. </span></p> <p class="MsoNormal"><span><strong>Methodology:</strong> Prospective case cohort study of children with confirmed dengue. </span></p> <p class="MsoNormal"><span><strong>Results</strong>: 303 children were included and divided into two groups – the dengue fever group and the complicated dengue group based on the WHO clinical classification. The clinical and laboratory parameters were analysed individually, cut offs identified by ROC curves and compared for significance between the two groups. The parameters that emerged were hypotension, PCV ≥ 42%, platelet count ≤ 75000 cells/cumm, WBC ≥ 7000 cells/cumm, and ALT ≥ 70U/L.</span> <span>Using the adjusted odd's Ratio, and coefficient, individual predictive scores were tabulated ranging from 0 to 3, with a total score of 0 to 7. A cut-off score of 2 was then identified based upon the sensitivity(84.13%) and specificity(72.50%) as the ideal score to predict complicated dengue. Internal validation of the score was done where the area under the curve for predicting complicated dengue was 0.86(95% CI 0.8-0.92) with a P value of <0.001.</span><strong><span> </span></strong></p> <p class="MsoNormal"><strong><span>Conclusion</span></strong><span>: </span><span>Our dengue predictive scoring system has been developed using five indicators, with a score of 2 and above out of 7, suggesting increased risk of developing complications. This has been validated internally and can be used to predict complicated dengue among children.</span></p>
SCoRe - Student Crowd Research. A german dataset of university students' written reflections on their participation in collaborative research-based learning focused on sustainability topics and using videos as a research tool.
<p>Schriftliche Reflexionen von 57 Studierenden als Prüfungsleistung im Rahmen des forschenden Lernens mit Video zu Nachhaltigkeitsthemen. Die Reflexionen wurden durch vorgegebene Fragen angeleitet. Die hier vorliegenden Texte sind, von den Studierenden selbst niedergeschriebene, Transkripte von Sprechtexten eines Self-Video-Casts. Es handelt sich dabei um eine benotete Prüfungsleistung einer universitätsübergreifenden Wahlpflicht-Lehrveranstaltung mit 1-3 Credit-Points.</p> <p>A dataset containing written reflections of 57 university students from several German universities and fields of study on their participation in collaborative research-based learning focused on sustainability topics and using videos as a research tool. These reflections were guided by given questions. The texts presented here are transcripts of spoken texts of self-video-casts, written down by the students themselves. The texts were graded as part of an inter-university elective course worth 1-3 credit points.</p>
Metabolic syndrome for the prognosis of postoperative complications after open pancreatic surgery in Chinese adult: a propensity score matching study
<p><strong>Background: </strong>To investigate the relationship between metabolic syndrome (MS) and postoperative complications in Chinese adults after open pancreatic surgery.</p> <p><strong>Methods: </strong>Relevant data were retrieved from the Medicalsystem® database of Changhai hospital (MDCH). All patients who underwent pancreatectomy from January 2017 to May 2019 were included, and relevant data were collected and analyzed. A propensity score matching (PSM) and a multivariate generalized estimating equation were used to investigate the association between MS and composite compositions during hospitalization. Cox regression model was employed for survival analysis.</p> <p><strong>Results: </strong>1481 patients were finally eligible for this analysis. According to diagnostic criteria of Chinese MS, 235 patients were defined as MS, and the other 1246 patients were controls. After PSM, no association was found between MS and postoperative composite complications (OR: 0.958, 95%CI: 0.715-1.282, P=0.958). But MS was associated with postoperative acute kidney injury (OR: 1.730, 95%CI: 1.050-2.849, P=0.031). Postoperative AKI was associated with mortality in 30 days and 90 days after surgery (P<0.001).</p> <p><strong>Conclusions: </strong>MS is not an independent risk factor correlated with postoperative composite complications after open pancreatic surgery. But MS is an independent risk factor for postoperative AKI of pancreatic surgery in Chinese population, and AKI is associated with survival after surgery.</p>
Supplementary information for: A continuous-score occupancy modeling framework for incorporating uncertain machine learning output in autonomous biodiversity surveys
<p><span>Ecologists often study biodiversity by evaluating species occupancy and the relationship between occupancy and other covariates. Occupancy models are now widely used to account for false absences in field surveys and to reduce bias in estimates of covariate relationships. Existing occupancy models take as inputs binary detection/non-detection observations of species at each visit to each site. However, autonomous sensing devices and machine learning models are increasingly used to survey biodiversity, generating a new type of observation record (i.e., continuous-score data) that reflects the model's confidence a species is present in each autonomously sensed file, instead of binary detection/non-detection data. These data are not directly compatible with traditional binary occupancy modeling methods.</span></p> <p><span>Here, we develop a new occupancy model that models continuous scores on a visit level as a Gaussian mixture, combining a distribution of scores for files that do contain the species of interest and a distribution of scores for files that do not. The model takes as input continuous scores for each autonomously sensed and classified file, along with an optional small number of binary, manually verified detection and non-detection annotations.</span></p> <p><span>We present a simulation study that shows that over a range of empirically realistic parameters, our model outperforms traditional occupancy models that are based on binary annotation alone. We also apply this new model to an empirical case study using data generated from five machine learning classifiers applied to autonomous acoustic recordings gathered in the eastern United States.</span></p> <p><span>Because our occupancy model generalizes allowable input data beyond binary observations, it is particularly well-suited to the increasing volume of machine learning classified data in ecology and conservation.</span></p>
Weighted Altmetric Scores to Facilitate Literature Analyses
<p>Following is the dataset used for the experimental analysis as part of the published conference paper, titled: <em>Weighted Altmetric Scores to Facilitate </em><em>Literature Analyses.</em></p>
REVEL (Rare Exome Variant Ensemble Learner) Scores
<p>REVEL is an ensemble method for predicting the pathogenicity of missense variants in the human genome. For more information, see <a href="https://sites.google.com/site/revelgenomics/">https://sites.google.com/site/revelgenomics/</a> and <a href="https://dx.doi.org/10.1016/j.ajhg.2016.08.016">https://dx.doi.org/10.1016/j.ajhg.2016.08.016</a>.</p>
Identifying a novel ferroptosis-related prognostic score for predicting prognosis in chronic lymphocytic leukemia
<p><span><strong>Background</strong>:</span><span> Chronic lymphocytic leukemia (CLL) is the most common leukemia in the western world. Although the treatment landscape for CLL is rapidly evolving, there are still some patients who remain drug resistance or disease refractory. Ferroptosis is a type of lipid peroxidation-induced cell death and has been suggested with a prognostic value in several cancers. Our research aims to build a prognostic model to improve risk stratification in CLL patients and facilitate more accurate assessment for clinical management.</span></p> <p><span><strong>Methods</strong>: </span><span>The differentially expressed ferroptosis-related genes (</span><span>FRGs) in CLL were filtered through univariate Cox regression analysis based on public databases. Least Absolute Shrinkage and Selection Operator (LASSO) Cox algorithms were performed to construct a prognostic risk model. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were performed to estimate the immune infiltration score and immune-related pathways. A total of thirty-six CLL patients in our center were enrolled in this study as a validation cohort. Moreover, a nomogram model was established to predict the prognosis.</span></p> <p><span><strong>Results</strong>: </span><span>A total of differentially expressed 15 FRGs with prognostic significance were screened out. After minimizing the potential risk of overfitting, we constructed a novel ferroptosis-related prognostic score (FPS) model with nine FRGs (AKR1C3, BECN1, CAV1, CDKN2A, CXCL2, JDP2, SIRT1, SLC1A5 and SP1), and stratified patients into low-risk and high-risk groups. Kaplan–Meier analysis showed that patients with high FPS had worse overall survival (OS) (P<0.0001) and treatment-free survival (TFS) (P<0.0001). ROC curves evaluated the prognostic prediction ability of the FPS model. Additionally, the immune cell types and immune-related pathways were correlated with the risk scores in CLL patients. In the validation cohort, the results confirmed </span><span>that the </span><span>high</span><span>-</span><span>risk group was related to worse OS (P<0.0001), progress-free survival (PFS) (P=0.0140) and TFS (P=0.0072). </span><span>In</span><span> the multivariate analysis, only FPS (P=0.011) and CLL-IPI (P=0.010) were independent risk indicators for OS. Furthermore, we established a nomogram including FPS and CLL-IPI which could strongly and reliably predict individual prognosis.</span></p> <p><span><strong>Conclusion</strong>: </span><span>A novel FPS model could be used in CLL for prognostic prediction. The model index may also facilitate the development of new clinical ferroptosis-targeted therapies in patients with CLL.</span></p>
Metabolic syndrome severity score and associated cardiovascular risk in postmenopausal women of Bangladesh
<p>This data help to construct metabolic syndrome severity score and seek its association with absolute cardiovascular risk.</p>
Bach10 Score-Informed Separation ISMIR2017
<p>This dataset accompanies the paper:<br> M.Miron, J.Janer,E.Gomez,"Monaural score-informed source separation for classical music using convolutional neural networks", ISMIR 2017, http://mtg.upf.edu/node/3806</p> <p>The files are based on Bach10 dataset which comprises 10 Bach chorales: http://music.cs.northwestern.edu/data/Bach10.html</p> <p>It comprises results in terms of SDR, SIR, SAR as .mat files for the methods presented in the paper.<br> Additionally, we include audio .wav files for the proposed score-informed source separation method using convolutional neural networks and for the score-informed NMF counterpart.</p> <p>The code is available at the github repository: https://github.com/MTG/DeepConvSep/tree/master/examples/bach10_scoreinformed</p> <p>We include the trained CNN model for the proposed approach, which can be used to separate Bach chorales with the code provided at the github repository. </p>
CHQ Score patient 1
<p>Cluster Headache Quality of life scale (CHQ) for a patient affected by Cluster Headache before and after the theraphy with high dose thiamine.</p>
Extended computing integrated curricula scored for K-12 CS standards
<p>Integrated computing curricula combine learning objectives in computing with those in another discipline, like literacy, math, or science, to give all students experience with computing, typically before they must decide whether to take standalone CS courses. One goal of integrated computing curricula is to provide an accessible path to an introductory computing course by introducing computing concepts and practices in required courses. This paper analyzed integrated computing curricula to determine which CS practices and concepts they teach and how extensively and, thus, how they prepare students for later computing courses. The authors conducted a content analysis to examine primary and lower secondary (i.e., K-8) curricula that are taught in non-CS classrooms, have explicit CS learning objectives (i.e., CS+X), and that took 5+ hours to complete. Lesson plans, descriptions, and resources were scored based on frameworks developed from the K-12 CS Framework, including programming concepts, non-programming CS concepts, and CS practices. The results found that curricula most extensively taught introductory concepts and practices, such as sequences, and rarely taught more advanced content, such as conditionals. Students who engage with most of these curricula would have no experience working with fundamental concepts, like variables, operators, data collection or storage, or abstraction in the context of a program. While this focus might be appropriate for integrated curricula, it has implications for the prior knowledge that students should be expected to have when starting standalone computing courses.</p>
Elevated perceived stress in university students due to COVID-19 pandemic: Potential contributing factors in a propensity-score matched sample
<p><strong>Open Data and Open Materials of: </strong></p> <p><strong>Elevated perceived stress in university students due to COVID-19 pandemic: Potential contributing factors in a propensity-score matched sample. </strong></p> <p><strong><em>Scandinavian Journal Of Psychology</em>.</strong></p> <p><strong>Abstract</strong></p> <p><strong>Objective</strong>: Onset of the Coronavirus Disease 2019 (COVID)-pandemic has increased students’ perceived burdens. The current study aimed to examine COVID-related changes and to identify potential factors that contribute to students’ stress.</p> <p><strong>Method</strong>: Adopting a cross-sectional cohort-study design, we examined perceived stress, depressive and anxiety symptoms with a specific focus on the role of study-related variables such as perceived study-related demands, study-related resources, academic procrastination, and stress enhancing beliefs. Two cohorts (<em>N</em><sub>pre-COVID</sub>=2175;<em>N</em><sub>COVID</sub>=959) were recruited at the same university and matched with regard to their propensity score (age, gender, semester).</p> <p><strong>Results</strong>: Compared to the pre-COVID cohort, university students in the COVID-cohort reported more perceived stress, more depressive and anxiety symptoms, more academic procrastination due to fear of failure, more stress-enhancing beliefs, more distress due to the housing situation, and more perceived study-related challenges (Cohen’s <em>d</em>=.15–.45). A stepwise regression analysis identified depressive symptoms, procrastination due to fear of failure, general self-efficacy, increased study demands, perceived difficulties with to self-organised learning, distress due to housing, and stress enhancing beliefs as predictors of perceived stress in the COVID-cohort.</p> <p><strong>Discussion</strong>: Findings suggest that the switch to online-only education increased the study-related burden for students, primarily due to exams being replaced by a greater amount of regular coursework and imposing demands on self-organised learning. Possibly, stress enhancing beliefs and procrastination due to fear of failure might have been elevated due to less opportunity for social referencing and lack of felt social support by peer students.</p> <p><strong>Conclusion</strong>: Experienced increased burden in students during the COVID pandemic was mostly accounted for by the lack of perceived individual resources rather than by the increase in objective study-related demands.</p>
LORIS: a logistic regression-based immunotherapy-response score
<p>This is a repository of input data and code for reproducing the paper titled "LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic, and genomic features" by Chang et al. (Nature Cancer 2024).</p> <p>Briefly, in this work, Chang et al. developed a new clinical score called the LOgistic Regression-based Immunotherapy-response Score (LORIS) using a transparent and concise 6-feature logistic regression model. LORIS outperforms previous signatures in ICB response prediction and can identify responsive patients, even those with low tumor mutational burden or tumor PD-L1 expression. Importantly, LORIS consistently predicts both objective responses and short-term and long-term survival across multiple cancer types. Moreover, LORIS showcases a near-monotonic relationship with ICB response probability and patient survival, enabling more precise patient stratification across the board. As the method is accurate, interpretable, and only utilizes a few readily measurable features, it could help improve clinical decision-making practices in precision medicine to maximize patient benefit.</p>
Data S2. NDCs Scores
<p>This dataset includes the scores assigned to the Nationally Determined Contributions (NDCs) for the years 2015 and 2020 from the 13 countries with the most mangrove-rich environments, along with the difference between these scores. </p>
Data from: Bristol stool scale: Patient versus expert score data
<p>The Bristol Stool Scale (BSS) is one of the most commonly used tools for evaluation of stool consistency.<strong> </strong>BSS ranges from 1-7 and each score is assigned to a given consistency of the feces. Self-reported characterizations can differ from an expert evaluation, and the reliability of BSS is unclear. The dataset consists of BSS scores by patients with inflammatory bowel disease collected throughout a 3-year follow-up, matched with scores assessed by experienced bioengineers (experts). The purpose of the study where data was collected was to compare patient scores to expert scores and hence determine the reliability of the BSS.</p>
Computing integrated activities scored for programming concepts
<p>Educators across disciplines are implementing lessons and activities that integrate computing concepts into their curriculum to broaden participation in computing. Out of myriad important introductory computing skills, it is unknown which—and to what extent—these concepts are included in these integrated experiences, especially when compared to concepts commonly taught in introductory computer science courses. Thus, it is unclear how integrated computing activities serve the goal of broadening participation in computing. To address this deficit, we compiled a database of 81 integrated computing activities, constructed a framework of fundamental programming concepts, and scored each activity in the database for the presence of each concept. The dataset also includes different activity features, including discipline, programming language, student age, and duration of activity. </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.