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709 results for “Mood”
MOOD_2017_RoadDensity_OSM_ER
<p>Road density for unclassified, minor, major, and autoroutes 2017. </p> <p><strong>Abstract: </strong></p> <p>This dataset was produced by extracting all road data from national-level Open Steet Map archives for 2017. The length of each type of road was calculated for a series of 5 square km grids covering the MOOD study area.</p> <p> </p> <p> <br><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br>Extent -24.5705863176307275,23.6115332000000535 : 44.5127470157025726,73.0115332000000308<br><strong>Spatial resolution:</strong><br>0.0083333 deg (approx. 1000 m) <br><strong>Temporal resolution:</strong><br>The year 2017<br><strong>Pixel values:</strong><br> Km of road per square km <br><strong>Source: </strong><br>Open Street Map<br><strong>Software used:</strong><br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0<br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p>
MOOD_GMTED_Elevation_Plus_1000
<p> </p> <p>Overview:</p> <p>This layer has been modified from the GMTED 90m resolution datasets to produce a 1km layer to match other MOOD covariates. The data are minimum elevation in metres plus 1000 to remove negative values.</p> <p> </p> <p>File naming scheme:</p> <p>vcmi30grdp1k.TIF and vcmi30grdp1k.TFW </p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Spatial extent:<br>Extent -24.0000000000000000,-38.5000000000000000 : 110.9999999999999432,86.9999999999999432</p> <p>Spatial resolution:<br>0.0083333 deg (approx. 1000 m)</p> <p>Accuracy:</p> <p>Based on World Geodetic System 1984 ensemble (EPSG:6326), which has a limited accuracy of at best 2 meters.</p> <p>Pixel values:<br>unit: meter</p> <p>Source: </p> <p>Copernicus Digital Elevation Model (DEM) for Europe derived from Copernicus Global 30 meter DEM dataset</p> <p>Software used:<br>ArcMap 10.8</p> <p>License: CC-BY-SA 4.0</p> <p>Processed by:<br>ERGO (Environmental Research Group Oxford) <a href="https://ergoonline.co.uk/" target="_blank" rel="noopener">https://ergoonline.co.uk/</a> for the H2020 MOOD project</p> <p> </p> <p> </p> <p> </p>
MOOD_2021_FloodRisk_JRC
<p>Floodrisk, 10 year return period, 1km. </p> <p><strong>Abstract:</strong></p> <p>The flood risk dataset downloaded from https://data.jrc.ec.europa.eu/collection/id-0054 has been windowed to the MOOD extent for access by project partners. According to JRC "The maps have been developed using hydrological and hydrodynamic models, driven by the climatological data of the European and Global Flood Awareness Systems (EFAS and GloFAS). European-scale maps comprise most of the geographical Europe and all the river basins entering the Mediterranean and Black Seas in the Caucasus, Middle East and Northern Africa countries."</p> <p> </p> <p><strong>File naming scheme: </strong></p> <p>flood risk in TIF format for 10 years : <a href="../api/records/12799470/draft/files/erfloodMapGL_rp10y1km.tif/content" target="_blank" rel="noopener">erfloodMapGL_rp10y1km.tif </a></p> <p>JRC Guide: <a href="../api/records/12799470/draft/files/JRC%20cdatafloodrisk.pdf/content" target="_blank" rel="noopener noreferrer">JRC cdatafloodrisk.pdf</a> </p> <p><strong>Projection </strong>+ EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716</p> <p><strong>Spatial resolution:</strong><br>0.0083333 deg (approx. 1000 m)</p> <p><strong>Temporal Resolution:</strong></p> <p>10 year period</p> <p><strong>Pixel values:</strong><br>unit: meter</p> <p><strong>Source: </strong></p> <p> JRC: https://data.jrc.ec.europa.eu/collection/id-0054</p> <p><strong>Software used:</strong><br>ArcMap 10.8</p> <p><strong>License:</strong> CC-BY-SA 4.0</p> <p><strong>Processed by</strong>:<br>ERGO (Environmental Research Group Oxford) <a href="https://ergoonline.co.uk/" target="_blank" rel="noopener">https://ergoonline.co.uk/</a> for the H2020 MOOD project</p>
MOOD_2019_SurfaceWater_PROBA_1k
<p>PROBA V 2019 Permanent and Seasonal water 1km. </p> <p><strong>Abstract: </strong></p> <p>These layers were extracted for the MOOD extent from the Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe (Marcel Buchhorn, Bruno Smets, Luc Bertels, Bert De Roo, Myroslava Lesiv, Nandin-Erdene Tsendbazar, Martin Herold, & Steffen Fritz. (2020). Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe (V3.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3939050). </p> <p>Several kilometre resolution derivatives have been produced from the original 100m resolution dataset for seasonal and permanent water categories: </p> <p> </p> <p><strong>File naming scheme:</strong> <br>Distance to permanent or seasonal water (erprobapermseaswatdistm1kmll.tif); </p> <p>Percentage of seasonal water (erprobavLLC2019pcseaswat1km.tif);</p> <p>Percentage of permanent water (erprobavLLC2019pcpermwat1km.tif)<br> <br><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716</p> <p><br><strong>Spatial resolution:</strong><br>0.0083333 deg (approx. 1000 m) </p> <p><br><strong>Temporal resolution:</strong><br>seasonal and permament </p> <p><br><strong>Pixel values:</strong><br>Meter and Percentage</p> <p><br><strong>Source: </strong><br>The Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019</p> <p><br><strong>Software used:</strong><br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0<br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p> <p> </p>
MOOD_2020_StandardMapping_AnalysisPolygons
<p>MOOD standard Mapping and Analysis polygons. </p> <p><strong>Abstract: </strong></p> <p>Standard extents and polygons were defined at the start of the MOOD project. There sets of standatd geographies are provided: <br> a) A series of decimal degree grids (0.05, 0.1, 0.2, 0.5 ) in moodgrids.zip <br> b) raster extents in EPRS ( molandmasketrslaeaepsg3035.tif) and geographic projection (molatlonglandmask.tif) in moodpolygonsjanmasks21.zip; and <br> c) standard Administrative unit Polygons adopted from the Vectornet Project : one with relatively equal sized units designed for mapping (vectornetMAPforMOODjan21.shp) and one more suited to analysing and entering data recorded by admin unit that has consistent NITS 3 or Gaul 2 polygons (VectornetDATAforMOODjan21.shp) also in moodpolygonsjanmasks21.zip</p> <p> <br><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br><br>-73.2634658809999451,18.9632860000000392 : 69.0703202920000763,83.6274185180000700<br><br><strong>Software used:</strong><br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0</p> <p><br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p>
MOOD_2020_Worldpop_HumanPopulation_Age_Gender
<p>Worldpop Human 2020 population by Age and Gender. </p> <p><strong>Abstract:</strong></p> <p>Human population estimates per pixel were extracted from MOOD partner Worldpop (www.worldpop.org) datasets for the MOOD extent. Gender age categories were summed to provide datasets for all males, all females, and the total population. </p> <p> </p> <p><strong>File naming scheme:</strong> </p> <p>Filenames are as follows (MOWPGGGRRYY-OOCog.TIF where GGG =-gender (male = MAL, female = FEM), both = TOT); RR = Greater than (gt) or Less than (lt); YY = mimimum age; OO= Maximum age</p> <p><br><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)<br><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,10.0000000000000000 : 68.9999995960000092,81.9999997120000046</p> <p><strong>Spatial resolution:</strong><br>0.0083333 deg (approx. 1000 m) </p> <p><strong>Temporal resolution:</strong><br> The year 2020</p> <p><strong>Pixel values:</strong><br>Human population estimates per pixel </p> <p><strong>Source: </strong><br>Worldpop (www.worldpop.org) datasets</p> <p><br><strong>Software used:</strong><br>The software used for map production is ESRI ArcMap 10.8</p> <p><br><strong>License: </strong>CC-BY-SA 4.0</p> <p><br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p>
The CoVidAffect dataset of mood variations following the COVID-19 outbreak in Spain
<p>Latest Update of the CoVidAffect dataset</p>
Data for "The Role of Mood and Stress in Physical Activity Engagement: Insights from Ecological Momentary Assessment
<p>Data collected in the SmartPA study in a joint effort from the Department of Psychology at the University of Salzburg (Jens Blechert) and the Ludwig Boltzmann Institute for Digital Health and Prevention Salzburg.</p>
Dataset: Relative Sentiment Tactical Allocation ETF (MOOD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
14-day smartphone ambulatory assessment of depression symptoms and mood dynamics in a general population sample: comparison with the PHQ-9 depression screening
<p>This dataset contains 14 days of ambulatory assessment (AA) depression symptoms and mood ratings with timestamps, a retrospective Patient Health Questionnaire (PHQ-9) assessment and the demographic variables age and gender.</p> <p>The AA was conducted with users of the mobile mental health / depression screening app "Moodpath". ICD-10 depression symptoms were assessed with the following questions:</p> <p>ICD-10 symptom 1: "depressed mood"<br> q1 = Are you feeling depressed?<br> q2 = Are you feeling hopeless?</p> <p>ICD-10 symptom 2: "loss of interest and enjoyment"<br> q3 = Do you feel like you are not interested in anything right now?<br> q4 = Do you have less pleasure in doing things you usually enjoy?</p> <p>ICD-10 symptom 3: "increased fatigability"<br> q5 = Do you currently have considerably less energy? <br> q6 = Are your everyday tasks making you very tired currently?</p> <p>ICD-10 symptom 4: "reduced concentration and attention"<br> q11 = Is it hard for you to make decisions currently? <br> q12 = Is it hard for you to concentrate currently?</p> <p>ICD-10 symptom 5: "reduced self-esteem and self-confidence"<br> q7 = Is your self-confidence clearly lower than usual?<br> q8 = Are you feeling up to your tasks? </p> <p>ICD-10 symptom 6: "ideas of guilt and unworthiness"<br> q9 = Are you blaming yourself currently? <br> q10 = Do you think you are worth less than others right now?</p> <p>ICD-10 symptom 7: "bleak and pessimistic views of the future"<br> q46 = Are you thinking that you will be doing well in the future? <br> q47 = Are you looking hopefully into the future?</p> <p>ICD-10 symptom 8: "ideas or acts of self-harm or suicide"<br> q16 = Are you thinking about death more often than usual? </p> <p>ICD-10 symptom 9: "disturbed sleep"<br> q13 = Did you sleep badly last night? </p> <p>ICD-10 symptom 10: "diminished appetite"<br> q14 = Do you have less or no appetite today? </p>
Lithium for Suicidal Behavior in Mood Disorders
ClinicalTrials.gov study NCT01928446. IPD Sharing: YES. Countries: 1. Publications: 1.
Treatment for Migraine and Mood (Team-M)
ClinicalTrials.gov study NCT04992494. IPD Sharing: YES. Countries: 1. Publications: 0.
Dataset of Emotional intelligence and mood in the beauty contest game
<p>This site provides access to the dataset in the article "Emotional Intelligence and Mood in the Beauty Contest Game: An Experimental Study." The study explores emotional intelligence and mood's impact on strategic decision-making within the Beauty Contest Game framework. Results offer compelling evidence of their influence on strategic guessing and reasoning depth. The dataset includes responses from 96 participants, assessing emotional intelligence, mood categories, and strategic decision details. This resource is crucial for understanding and validating study findings, shedding light on decision-making dynamics and implications for psychology, economics, and organizational behavior. The dataset's availability encourages further research on emotions in decision-making, serving as a foundation for exploring the interrelationship between emotional intelligence, mood, and strategic behavior.</p>
H-2020 MOOD Scoping review of Leptospirosis, Influenza A and Chikungunya on the environmental covariates
<p>The dataset contains quantitative data on the environmental covariates associated with influenza A, Chikungunya, and Leptospirosis retrieved from scientific papers through a standardized search on PubMed, Embase, Web of Science, and Scopus. Inclusion criteria were data on the association between disease and covariates, language (English or other EU languages), time frame (30 years), geographical location (Europe), and publication type. Studies without data or with non-original or duplicated data (reviews, editorials, letters, modeling studies with no data), lacking denominators or reference populations, unavailable full-texts, referring to data older than 2000 or gathered outside Europe, were excluded. The final time frame covered a period from 2000 to 2022.</p> <p>The important environmental covariates were extracted with the associated quantitative information, and the related information on the diseases. The covariates were submitted to a revision process to label them according to a labeling system agreed upon among a group of experts within the MOOD (grant agreement No 874850; <a href="https://mood-h2020.eu/" target="_blank" rel="noopener noreferrer">https://mood-h2020.eu/</a>) project consortium.</p>
Perceptual learning with mood manipulations -- Children and adults
<p>A .csv including trial-by-trial data from a texture detection visual perceptual learning experiment (see, e.g., Ahissar & Hochstein, 2001). Each trial [row] includes whether the participant responded correctly ('acc'), the stimulus onset asynchrony ('SOA'), the trial number ('trialNum'), the age group ('ageC'; children coded -.5 and adults coded .5), the coding of manipulation condition by arousal ('arousal'; control coded -.5 and non-control coded .5), the deidentified subject ID ('subID'), and the name of the manipulation condition ('valence'; options are "Control", "Stress", or "Positive").</p>
Brief structured respiration practices enhance mood and reduce physiological arousal
<p><span>Controlled Breathwork practices have emerged as potential tools for stress management and well-being. Here we report a remote, randomized, controlled study (NCT05304000) of three different daily five-minute Breathwork exercises in comparison to an equivalent period of Mindfulness Meditation, over one month. The breathing conditions are 1) Cyclic Sighing, which emphasizes prolonged exhalations, 2) Box Breathing, which is equal duration of inhalations, breath retentions and exhalations, and 3) Cyclic Hyperventilation with Retention, with longer inhalations and shorter exhalations. The primary endpoints are improvement in mood and anxiety as well as reduced physiological arousal (respiratory rate, heart rate, and heart rate variability). Using a mixed-effects model, we show that Breathwork, especially the exhale-focused Cyclic Sighing produces greater improvement in mood (p < 0.05) and reduction in respiratory rate (p < 0.05) compared to Mindfulness Meditation. Daily 5-minute Cyclic Sighing has promise as an effective stress management exercise. </span></p>
Study of Mood Effects of Varenicline (Chantix) in Depressed Outpatient Smokers
ClinicalTrials.gov study NCT00525837. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Menopause Transition: Estrogen Variability, Stress Reactivity and Mood
ClinicalTrials.gov study NCT03003949. IPD Sharing: YES. Countries: 1. Publications: 1.
Effects of Bright Light on Sleep Quality, Fatigue , and Mood Symptoms in Survivors With Gynecologic Cancer
ClinicalTrials.gov study NCT07132580. IPD Sharing: YES. Countries: 1. Publications: 3.
The Effects of Light Therapy on Circadian Rhythms, Sleep, and Mood in Postpartum Depression
ClinicalTrials.gov study NCT02769858. IPD Sharing: NO. Countries: 1. Publications: 1.
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OpenNeuro
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