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2,168 results for “MINDFULNESS.”
Mindfulness and CBT for Sleep
ClinicalTrials.gov study NCT04736056. IPD Sharing: NO. Countries: 1. Publications: 1.
A Mind Body Intervention to Reduce Symptoms Among People Aging With HIV
ClinicalTrials.gov study NCT03840525. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Mindfulness Meditation for the Treatment of Women With Comorbid PTSD and SUD
ClinicalTrials.gov study NCT02755103. IPD Sharing: NO. Countries: 1. Publications: 1.
Design and implementation of a brief digital mindfulness and compassion training app for health care professionals: cluster randomized controlled trial
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Data from: Nematode mind: Exploring the role of the RNAi pathway in learning, memory and beyond
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Data from: Tolerant and despotic macaques show divergent temperament but similar theory of mind
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Mind the gap: natural cleft palates reduce biting performance in bats
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Bear in mind! Bear presence and individual experience with calf survival shape the selection of calving sites in a long-lived solitary ungulate
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Data from: Online prenatal trial in mindfulness sleep management (OPTIMISM)
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Minding the gap: Learning and visual scanning behaviour in nocturnal bull ants
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Brief mindfulness coaching enhances selective attention in medical scientists: A pilot study
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Spontaneous pupillary oscillations increase during mindfulness meditation
<p>This is the dataset reported in the manuscript "Spontaneous pupillary oscillations increase during mindfulness meditation" currently under review</p>
Data from: Out of sight, out of mind: Widespread nuclear and plastid-nuclear discordance in the flowering plant genus Polemonium (Polemoniaceae) suggests widespread historical gene flow despite limited nuclear signal
Phylogenomic data from a rapidly increasing number of studies provide new evidence for resolving relationships in recently radiated clades, but they also pose new challenges for inferring evolutionary histories. Most existing methods for reconstructing phylogenetic hypotheses rely solely on algorithms that only consider incomplete lineage sorting as a cause of intra- or inter-genomic discordance. Here, we utilize a variety of methods, including those to infer phylogenetic networks, to account for both incomplete lineage sorting and introgression as a case for nuclear and cytoplasmic-nuclear discordance using phylogenomic data from the recently radiated flowering plant genus Polemonium (Polemoniaceae), an ecologically diverse genus in Western North America with known and suspected gene flow between species. We find evidence for widespread discordance among nuclear loci that can be explained by both incomplete lineage sorting and reticulate evolution in the evolutionary history of Polemonium. Furthermore, the histories of organellar genomes show strong discordance with the inferred species tree from the nuclear genome. Discordance between the nuclear and plastid genome is not completely explained by incomplete lineage sorting, and only one case of discordance is explained by detected introgression events. Our results suggest that multiple processes have been involved in the evolutionary history of Polemonium and that results from the plastid genome do not accurately reflect species relationships. We discuss several potential causes for this cytoplasmic-nuclear discordance, which emerging evidence suggests is more widespread across the Tree of Life than previously thought.
Replication package - The Mind Is a Powerful Place: How Showing Code Comprehensibility Metrics Influences Code Understanding
<p>Replication package for:</p> <p>Marvin Wyrich, Andreas Preikschat, Daniel Graziotin and Stefan Wagner. The Mind Is a Powerful Place: How Showing Code Comprehensibility Metrics Influences Code Understanding. To appear in: Proceedings of the 43rd International Conference on Software Engineering (ICSE ’21), Madrid, Spain.</p> <p>- The `data` folder contains dataset, R analysis script, and figures for the paper<br> - the `material` folder contains the snippets used for the experimental tasks (`code snippets` subfolder), the environment in which participants had to understand the code (`environment` subfolder) and the task sheet form to evaluate code comprehension.</p> <p>The R analysis script allows to reproduce all statistical analysis steps of the paper as well as its figures. We recommend calling `setwd()` before running the script contents, so that the dataset can be properly loaded.</p> <p>For ethics and privacy reasons, we had to drop the following potentially identifying fields from the openly released dataset, because the fields might enable participants to identify their peers:</p> <p>- Age<br> - Gender<br> - Subject (study plan)<br> - Program<br> - Semester no.</p> <p>These missed details will still allow full reproducibility of the study.</p>
final scores of mind map strategy
<p>the data shows the total score of each student, the mean scores of vocabulary and morphemes, reading, listening, speaking, and writing, and the mean hours of self medical English learning per week.</p>
AMEO - Aspiring Minds' Employment Outcomes 2015
<p>India produces 1.5 million engineers every year. A relevant question is what determines the salary and the jobs these engineers are offered right after graduation. Various factors such as college grades, candidate skills, proximity of the college to industrial hubs, the specialization one is in, market conditions for specific industries determine this. </p> <p>The dataset contains various information about a set of engineering candidates and their employment outcomes. For every candidate, the data contains both the profile information along with their employment outcome information. Candidate Profile Information includes: </p> <ul> <li><strong>Scores on Aspiring Minds’ AMCAT</strong> – a standardized test of job skills. The test includes cognitive, domain and personality assessments</li> <li><strong>Personal information</strong> like gender, date of birth, etc.</li> <li><strong>Pre-university information</strong> like high school grades, high school location</li> <li><strong>University information</strong> like GPA, college major, college reputation proxy.</li> <li><strong>Demographic information</strong> like location of college, candidates’ permanent location</li> </ul> <p>Employment Outcome Information includes:</p> <ul> <li>First job annual salary</li> <li>First job title</li> <li>First job location</li> </ul> <p>This is the only data set where we have employment outcomes together with scores on a standardized job test, which makes this very unique. Other such data sets either do not test scores at all or scores on pre-university tests</p> <p><strong>Data Collection</strong></p> <p>A million undergraduates take AMCAT every year as a way to get job credentials and feedback to improve themselves. Candidates are tested on the following skills –</p> <ul> <li><strong>English Language, Logical Ability and Quantitative Ability</strong> – these are IRT based adaptive modules. More information here on what IRT based adaptive tests are.</li> <li><strong>Personality</strong> – Big Five based personality instrument. More information here.</li> <li><strong>Skill tests</strong> – Chosen by candidates based on their interest</li> </ul> <p>These assessments are validated against on-job performance and show a validity between 0.3-0.55 (Learn more about test validity here - http://www.centerforpubliceducation.org/Main-Menu/Evaluating-performance/A-guide-to-standardized-testing-The-nature-of-assessment). These scores are used by 2000+ companies. <br /> <br /> Random AMCAT takers were surveyed via email wherein they provided information on the dependent variables in this dataset – the jobs they are in and their corresponding annual salaries. Corresponding independent information about the candidates was recorded at the time of them taking AMCAT. </p> <p> </p>
Mindfulness, Psychological Capital, Soft Skills, and Refugees' Psychological Wellbeing
<p>The data concerns the role of positive psychological attributes in the wellbeing of refugees in the context of a less developed country in Africa</p>
CoNEXT21: Mind the Gap: Multi-hop IPv6 over BLE in the IoT - Experiment Result Data
<p>This dataset contains the all raw experiment data that was used in our paper "Mind the Gap: Multi-hop IPv6 over BLE in the IoT" published at CoNEXT21.</p>
Mindfulness publications-2021-08-20
<p>The original data of mindfulness publications</p>
Mind the Gap: Reach and Mechanical Diversity of Searcher Shoots in Climbing Plants
<p>This dataframe corresponds to the article Hattermann et al. "Mind the Gap: Reach and Mechanical Diversity of Searcher Shoots in Climbing Plants"</p> <table> <tbody> <tr> <td>Variable Abbreviation</td> <td>Description of the variable</td> <td>Type of variable</td> <td>Units</td> </tr> <tr> <td>ID</td> <td>Sample identification label at the shoot level</td> <td>Nominal variable</td> <td> </td> </tr> <tr> <td>Taxa</td> <td>Species name</td> <td>Nominal variable</td> <td> </td> </tr> <tr> <td>family</td> <td>Family name</td> <td>Nominal variable</td> <td> </td> </tr> <tr> <td>ds</td> <td>Biomes where shoots have been sampled - "temperate" corresponds to Montpellier, south of France and tropical corresponds to Sinnamary, French Guiana </td> <td>Nominal variable</td> <td> </td> </tr> <tr> <td>Leaf_expanded</td> <td>Indicates whether a shoot has been considered with at least one expanded leaf (1) or with no leaves (0)</td> <td>Discrete variable</td> <td> </td> </tr> <tr> <td>Group</td> <td>Functional group based on the climbing habit at the species level</td> <td>Nominal variable</td> <td> </td> </tr> <tr> <td>reach</td> <td>Distance in a straight line from the base to the apex of the searcher shoot (here called the “reach”)</td> <td>Continuous variable</td> <td>cm</td> </tr> <tr> <td>length</td> <td>Length of the searcher shoot</td> <td>Continuous variable</td> <td>cm</td> </tr> <tr> <td>A1_diam_basal</td> <td>Basal diameter of the searcher shoot (measured at the base of the searcher shoot)</td> <td>Continuous variable</td> <td>mm</td> </tr> <tr> <td>N_leaves</td> <td>Number of leaves</td> <td>Discrete variable</td> <td> </td> </tr> <tr> <td>seco_mom_I</td> <td>Second moment of area (I) of the measured basal segment (in 4-point bending) of the searcher shoot</td> <td>Continuous variable</td> <td>mm^4</td> </tr> <tr> <td>young_mod_E</td> <td>Structural Young's modulus (E) of the measured basal segment (in 4-point bending) of the searcher shoot from I and EI</td> <td>Continuous variable</td> <td>MN.m-2</td> </tr> <tr> <td>flex_rig_EI</td> <td>Flexural bending rigidity (EI) of the measured basal segment (in 4-point bending) of the searcher shoot</td> <td>Continuous variable</td> <td>N.mm^2</td> </tr> <tr> <td>total_freshmass</td> <td>Freshmass of the searcher shoot (including stems, laminas and petioles)</td> <td>Continuous variable</td> <td>g</td> </tr> <tr> <td>lam_freshmass</td> <td>Lamina freshmass borne by the searcher shoot</td> <td>Continuous variable</td> <td>g</td> </tr> <tr> <td>total_drymass</td> <td>Total drymass of the searcher shoot (including stems, laminas and petioles)</td> <td>Continuous variable</td> <td>g</td> </tr> <tr> <td>lam_drymass</td> <td>Lamina drymass borne by the searcher shoot</td> <td>Continuous variable</td> <td>g</td> </tr> <tr> <td>lam_area</td> <td>Lamina fresh area of the searcher shoot</td> <td>Continuous variable</td> <td>mm^2</td> </tr> <tr> <td>A_Pith</td> <td>Medullary parenchyma cross-sectional area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^2</td> </tr> <tr> <td>A_Pith_fibers</td> <td>Medullary fibre cross-sectional area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^2</td> </tr> <tr> <td>A_Xylem</td> <td>Xylem vessel and fibres cross-sectional area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^2</td> </tr> <tr> <td>A_Xylem_ray</td> <td>Xylem parenchymatous ray cross-sectional area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^2</td> </tr> <tr> <td>A_Phloem</td> <td>Phloem cross-sectional area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^2</td> </tr> <tr> <td>A_Cortex</td> <td>Cortical parenchyma cross-sectional area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^2</td> </tr> <tr> <td>A_Sclereids</td> <td>Cortical sclereids cross-sectional area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^2</td> </tr> <tr> <td>A_Cort_fibers</td> <td>Cortical fibre cross-sectional area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^2</td> </tr> <tr> <td>A_Collenchyma</td> <td>Collenchyma cross-sectional area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^2</td> </tr> <tr> <td>A_Periderm</td> <td>Periderm cross-sectional area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^2</td> </tr> <tr> <td>I_Pith</td> <td>Medullary parenchyma second moment of area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^4</td> </tr> <tr> <td>I_Pith_fibers</td> <td>Medullary fibre second moment of area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^4</td> </tr> <tr> <td>I_Xylem</td> <td>Xylem vessel and fibres second moment of area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^4</td> </tr> <tr> <td>I_Xylem_ray</td> <td>Xylem parenchymatous ray second moment of area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^4</td> </tr> <tr> <td>I_Phloem</td> <td>Phloem second moment of area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^4</td> </tr> <tr> <td>I_Cortex</td> <td>Cortical parenchyma second moment of area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^4</td> </tr> <tr> <td>I_Cort_fibers</td> <td>Cortical sclereid second moment of area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^4</td> </tr> <tr> <td>I_Sclerites</td> <td>Cortical fibre second moment of area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^4</td> </tr> <tr> <td>I_Collenchyma</td> <td>Collenchyma second moment of area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^4</td> </tr> <tr> <td>I_Periderm</td> <td>Periderm second moment of area at the base of the searcher shoot</td> <td>Continuous variable</td> <td>mm^4</td> </tr> </tbody> </table>
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.