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Early Predicting COVID-19 Severity Value with Extracellular Vesicles and Extracellular RNAs
GEO Series GSE158877. Homo sapiens. 41 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Diagnostic Value and Clinical Significance of Potential BRAF-related Genes in Papillary Thyroid Carcinoma
GEO Series GSE309678. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
UBV MEAN VALUES V1.0
This is a compilation of mean U-B and B-V color indices of asteroids. This data file was provided by E.F. Tedesco, MRC and is a *preliminary* version. The version actually published will include UBV observations published through 1994 and the individual, referenced and commented, observations from which the average value was obtained. This dataset presents Johnson system U-B and B-V colors (or colors transformable to them) for asteroids, obtained between 1951 and 1989 and published through mid-1990. In addition, it uses Ted Bowell's unpublished observations from the Lowell UBV Asteroid Survey. The 978 mean U-B color indices are derived from 3,476 U-B observations. The 1,020 mean B-V color indices are derived from 4,010 B-V observations.
The prognostic value of the detection of microbial translocation in the blood of colorectal cancer patients
<p>Results of a retrospective analysis aiming to investigate whether microbial translocation occurs in colorectal cancer patients and to evaluate the usefulness of PCR for diagnosis of such translocation in the blood of these patients before treatment initiation. The secondary objective of the study was the correlation of the presence of previously investigated by our group toll-like and vitamin D receptor gene polymorphisms with the detection of microbial DNA fragments in the blood of colorectal cancer patients. </p> <p>The detection of microbial DNA fragments in CRC patients highlights the role of these microbes in cancer development, progression and patients’ survival. Clearly, this increased risk in colorectal patients should be taken into consideration by the clinicians when planning any surgical intervations or during chemotherapy</p>
MIC values from MBL-producing Enterobacterales collection
<p>Raw minimum inhibitory concentration values (in µg/mL) obtained for the MBL-producing Enterobacterales collection following the agar dilution standard procedure.</p>
Data associated with the manuscript entitled The impact of item-writing flaws and item complexity on examination item difficulty and discrimination value.
<p>Data spreadsheet for accompanying manuscript.</p>
Understanding E-waste Recycling Behaviour : The Effects of Environmental Values, Habit, Moral Norm and Situational Factors in the Theory of Planned Behaviour
<p>Data collected for ascertaining the effects of environmental values, moral norm, habit and situational factors on the Theory of Planned Behaviour in e-waste recycling behaviour. </p>
Data and code for project "What makes users click: the effect of news values negativity and surprise in news headlines on the CTR"
<p>This repository includes the following files and corresponding Python codes saved in Jupiter Notebooks:</p> <p><strong>upworthy-archive-confirmatory-packages-03.12.2020.csv:</strong> Original dataset downloaded from The Upworthy Research Archive (https://osf.io/jd64p/)</p> <p><strong>cleaned_data.csv</strong>: <strong>upworthy-archive-confirmatory-packages-03.12.2020.csv</strong> cleaned with <strong>notebook_clean_data.ipynb</strong></p> <ul> <li>Data sorted by <em>clickability_test_id</em></li> <li>Headlines with 0 clicks deleted</li> <li>Variables <em>ctr</em>, <em>mean_ctr</em>, and <em>lift</em> created</li> <li>Variable <em>winner</em> converted to dummy variable</li> <li>Only kept first occurrence of headlines that occur multiple times in the dataset per test</li> <li>Deleted tests with only one headline</li> </ul> <p><strong>sample_manual_annotations.csv</strong>: sample of 300 headlines of <strong>cleaned_data.csv </strong>retrieved with <strong>notebook_sample_manual_annotations.ipynb</strong></p> <p><strong>inter-annotator_sentiment.csv</strong>: input for <strong>notebook_inter-annotator_scores_confusion_matrix.ipynb</strong> to calculate inter-annotator agreement scores with regard to annotating sentiment</p> <p><strong>inter-annotator_emotion.csv</strong>: input for <strong>notebook_inter-annotator_scores_confusion_matrix.ipynb</strong> to calculate inter-annotator agreement scores with regard to annotating emotion</p> <p><strong>input_sentiment.tsv</strong>: input for <strong>notebook_sentiment_emotion_test_set.ipynb </strong>for sentiment analysis on sample of 300 headlines in <strong>sample_manual_annotations.csv</strong></p> <p><strong>input_emotion.tsv</strong>: input for <strong>notebook_sentiment_emotion_test_set.ipynb </strong>for emotion analysis on sample of 300 headlines in <strong>sample_manual_annotations.csv</strong></p> <p><strong>input_all_headlines.tsv</strong>: input for <strong>notebook_sentiment_emotion_final_annotation.ipynb</strong> to annotate all headlines in <strong>cleaned_data.csv</strong></p> <p><strong>final_dataset.csv</strong>: output from <strong>notebook_merge_dataframes.ipynb</strong> that merges all csv output files from <strong>notebook_sentiment_emotion_final_annotation.ipynb </strong>with <strong>cleaned_data.csv</strong></p> <p><strong>notebook_descriptive_statistics.ipynb</strong>: Python code to retrieve descriptive statistics of various variables in <strong>cleaned_data.csv</strong></p> <p><strong>NRC-Emotion-Lexicon-Wordlevel-v0.92.txt:</strong> NRC Emotion Lexicon used in <strong>notebook_sentiment_emotion_test_set.ipynb </strong>and<strong> notebook_sentiment_emotion_final_annotation.ipynb</strong></p>
INCREMENTAL PROGNOSTIC VALUE OF ARTERIAL ELASTANCE IN MILD-TO-MODERATE IDIOPATHIC PULMONARY FIBROSIS
<p><strong>PURPOSE: </strong>Previous reports suggested that poor pulmonary function was associated with increased arterial elastance (Ea) in patients with chronic obstructive pulmonary disease and systemic sclerosis. The mechanisms connecting pulmonary function and Ea have not yet been accurately studied in patients with idiopathic pulmonary fibrosis (IPF). The present study was designed to assess Ea in IPF patients without chronic severe pulmonary hypertension and to determine its prognostic role over a medium-term follow-up.</p> <p><strong>METHODS: </strong>This retrospective study included 60 consecutive patients with mild-to-moderate IPF (73.8±6.6 yrs, 75% males) and 60 controls matched by age, sex and cardiovascular risk factors. All patients underwent physical examination, spirometry, blood tests, modified Haller index (MHI, chest transverse diameter over the distance between sternum and spine) assessment, conventional transthoracic echocardiography implemented with speckle tracking analysis of left atrial positive global strain (LA-GSA+) and finally carotid Doppler ultrasonography, at basal evaluation. The effective arterial elastance index (EaI) was calculated as the ratio of end-systolic pressure to stroke volume index. During follow-up period, we evaluated the composite endpoint of 1) pulmonary or cardiovascular hospitalizations; 2) all-cause mortality.</p> <p><strong>RESULTS: </strong>At baseline, EaI was significantly higher in IPF patients than controls (4.1±1.3 vs 3.5±1.0 mmHg/ml/m<sup>2</sup>, p=0.01). EaI was strongly correlated to the following variables: C-reactive protein (CRP) (r=0.86), forced vital capacity (FVC) (r=-0.91), E/e’ ratio (r=0.91), LA-GSA+ (r=-0.92), common carotid artery-cross sectional area (CCA-CSA) (r=0.89) and MHI (r=0.86), in IPF patients. Mean follow-up time was 2.4±1.3 yrs. During follow-up, 12 patients died and 17 were hospitalized due to major adverse clinical events. At univariate Cox analysis, CRP (HR 1.51, 95%CI 1.25-1.82), FVC (HR 0.88, 95%CI 0.85-0.91), LA-GSA+ (HR 0.85, 95%CI 0.77-0.94), CCA-CSA (HR 1.12, 95%CI 1.03-1.22) and EaI (HR 2.43, 95%CI 1.75-3.37) were significantly associated with outcome. At multivariate Cox analysis, only EaI (HR 1.60, 95%CI 1.03-2.50) retained statistical significance. An EaI ≥4 mmHg/ml/m<sup>2 </sup>showed 100% sensitivity and 99.4% specificity for predicting outcome (AUC=0.98).</p> <p><strong>CONCLUSIONS: </strong>In patients with mild-to-moderate IPF, an EaI ≥4 mmHg/ml/m<sup>2 </sup>is a negative prognostic factor over a medium-term follow-up.</p>
Data set from Stiffness of the Masseter Muscle in Children-Establishing the Reference Values in the Pediatric Population Using Shear-Wave Elastography
<p>Data set from Stiffness of the Masseter Muscle in Children-Establishing the Reference Values in the Pediatric Population Using Shear-Wave Elastography</p>
Power values
<p>Power values</p>
Size spectrum values in stream fish communities
<p>Size spectrum values and the main predictors</p>
NDVI mean value for each entry of Bus Santé data file.
<p>This data set contains health and social data that were collected by Bus santé, a cross-sectional population-based study that collects information on cardiovascular risk factors. Using QGIS, a layer containing a 100 m buffer zone around each individual was made to have an estimate of the area inside which each person lives.<br> Subsequently, in order to obtain one file with both the NDVI and the health information, it was necessary to compute intersection between the hectometric NDVI file and the buffer layer: the median NDVI value inside the buffer zone for each person was calculated.</p>
Daily SPEI-90 Days Values – Elevation Below 1500 m a.s.l., 1950–2023
<table> <tbody> <tr> <td> <p><strong>Science Case Name</strong> </p> </td> <td> <p>Multi-Hazards in the Downstream Area of the Adige River Basin. </p> </td> </tr> <tr> <td> <p><strong>Dataset Name/Title</strong> </p> </td> <td> <p>Daily SPEI-90 Days Values – Elevation Below 1500 m a.s.l., 1950–2023</p> </td> </tr> <tr> <td> <p><strong>Dataset Description</strong> </p> </td> <td> <p>SPEI (Standardized Precipitation Evapotranspiration Index) data calculated using the E-OBS daily gridded data with a spatial resolution of 0.1° for the period from 1950 to 2023. The dataset describes the anomalies of the balance between precipitation and potential evapotranspiration with respect to the average conditions (Vicente-Serrano et al., 2010), considering both temperature and precipitation as inputs. </p> </td> </tr> <tr> <td> <p><strong>Key Methodologies</strong> </p> </td> <td> <p>Daily SPEI is here calculated at a time scale of 90 days (3 months). The SPEI calculation is based on the ‘SPEI’ R package by Beguería and Vicente-Serrano (2023) (https://github.com/sbegueria/SPEI) with the potential evapotranspiration derived from Hargreaves-Samani formulation. Based on E-OBS orography, SPEI values for altitudes above 1500 m a.s.l. are excluded from the current analysis to focus on low and mid-elevation areas only.</p> </td> </tr> <tr> <td> <p><strong>Temporal Domain</strong> </p> </td> <td> <p>1950–2023</p> </td> </tr> <tr> <td> <p><strong>Spatial Domain</strong> </p> </td> <td> <p>The dataset is provided over the [7.10, 44.10, 15.30, 49.10] spatial domain (min longitude, min latitude, max longitude, max latitude in WGS84, EPSG:4326).</p> </td> </tr> <tr> <td> <p><strong>Key Variables/Indicators</strong> </p> </td> <td> <p>Standardized Precipitation Evapotranspiration Index 90-days (SPEI-90 days)</p> </td> </tr> <tr> <td> <p><strong>Data Format</strong> </p> </td> <td> <p>netCDF </p> </td> </tr> <tr> <td> <p><strong>Source Data</strong> </p> </td> <td> <p>E-OBS dataset (v29.0e)</p> </td> </tr> <tr> <td> <p><strong>Accessibility</strong> </p> </td> <td> <p>https://doi.org/10.5281/zenodo.13778103</p> </td> </tr> <tr> <td> <p><strong>Stakeholder Relevance</strong> </p> </td> <td> <p>SPEI represents a valuable insight for researchers working on drought related issues, technical experts from public agencies and practitioners, as it is a comprehensive index able to identify drought hazard (dry event), while also considering temperature in the computation. Moreover, it can foster the use of Earth Observation when coupled with event identification methods by providing a robust meteorological-based event identification. This can be then further refined by the use of higher spatial resolution EO data capable of capturing subtle spatial patterns (e.g., soil moisture as influenced by droughts).</p> </td> </tr> <tr> <td> <p><strong>Limitations/Assumptions</strong> </p> </td> <td> <p>Excludes areas above 1500 m a.s.l. </p> </td> </tr> <tr> <td> <p><strong>Additional Outputs/Information</strong> </p> </td> <td> <p>DBSCAN 3D Clusters of SPEI-90 Days Values – Italian NUTS3 (ITH31, 32, 34, 35, 36, 37), 1950–2023 (https://zenodo.org/records/13785996) </p> <p>The dataset access is currently restricted due to pending related publication.</p> </td> </tr> <tr> <td> <p><strong>Contact Information</strong></p> </td> <td> <p>Crespi, Alice (Center for Climate Change and Transformation, Eurac Research, Bolzano, Italy) - Data manager<br>Lemus i Cánovas, Marc (Center for Climate Change and Transformation, Eurac Research, Bolzano, Italy, CRETUS, Non-linear Physics Group, Universidade de Santiago de Compostela, Galicia, Spain)- Data curator<br>Maines, Elena (Center for Climate Change and Transformation, Eurac Research, Bolzano, Italy) - Data curator<br>Masina, Marinella (CMCC Foundation - Euro-Mediterranean Center on Climate Change)- Data curator<br>Maraschini, Margherita (CMCC Foundation - Euro-Mediterranean Center on Climate Change) - Data curator<br>Ferrario, Davide Mauro (CMCC Foundation - Euro-Mediterranean Center on Climate Change) - Data curator<br>Furlanetto, Jacopo (CMCC Foundation - Euro-Mediterranean Center on Climate Change, National Biodiversity Future Center) - Data manager<br>Pittore, Massimiliano (Center for Climate Change and Transformation, Eurac Research, Bolzano, Italy) - Data manager<br>Terzi, Stefano (Center for Climate Change and Transformation, Eurac Research, Bolzano, Italy) - Data manager<br>Torresan, Silvia (CMCC Foundation - Euro-Mediterranean Center on Climate Change, National Biodiversity Future Center) - Data manager</p> </td> </tr> </tbody> </table>
Behavioral aspects of asset value by industry indices of the Moscow Stock Exchange
<p>The dataset contains data on the dynamics of industry indices of the Moscow Stock Exchange and the calculation of their abnormal returns during the Covid 19 pandemic</p>
The influence of behavioral aspects on the formation of the value of Russian assets during the period of coronavirus infection
<p>The dataset contains data on the dynamics of the industry indices of the Moscow Stock Exchange and the index IMOEX, the dynamics of macroeconomic factors, abnormal returns</p>
Determining the Value of Information for Minimizing Controller Taskload: A Graph-Based Approach
not available
UBV MEAN VALUES V1.1
U-B and B-V color indices of asteroid, compiled by E. Tedesco.
Tricuspid annulus 3D reference values
<p>3D echo data about tricuspid annulus geometry from healthy subjects</p>
ADDED Echo Value
<p>Added Echo Value dataset</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.