21978 research outputs found
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Norwegian Newspaper Coverage of Climate Change or Global Warming, 2000-2024 - December 2024
No full textThe Media and Climate Change Observatory Data monitors 131 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.</p
Swedish Newspaper Coverage of Climate Change or Global Warming, 2000-2024 - December 2024
No full textThe Media and Climate Change Observatory Data monitors 131 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.</p
US Newspaper Coverage of Climate Change or Global Warming, 2000-2024 - December 2024
No full textThe Media and Climate Change Observatory Data monitors 131 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.</p
Foundations of High-Performance Computing Micro-credential Checklist - Chi Chung Li
Get PDFThis micro-credentialed course provides a foundation for addressing computing-, memory-, or storage-intensive research problems using high-performance computing (HPC). Participants who complete the course will be able to navigate the Linux command line, apply data transfer protocols, find and use software on HPC, and use a scheduler to run batch and interactive jobs. Skills acquired in the course can greatly accelerate problem solving in the computational realm. </p
European Newspaper Coverage of Climate Change or Global Warming, 2004-2025 - January 2025
No full textThe Media and Climate Change Observatory Data monitors 131 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.</p
Finnish Newspaper Coverage of Climate Change or Global Warming, 2000-2025 - January 2025
No full textThe Media and Climate Change Observatory Data monitors 131 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.</p
New Zealand Newspaper Coverage of Climate Change or Global Warming, 2000-2025 - January 2025
No full textThe Media and Climate Change Observatory Data monitors 131 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.</p
Data from "2025 State of Open at the University of Colorado Boulder" Report
No full text
This data set contains five data files that were used to produce the "2025 State of Open at the University of Colorado Boulder" report:
1. CUBoulderOAFund2013_2024.csv contains data from articles funded by the CU Boulder Libraries Open Access Fund from 2013 to 2024. This data was collected by CU Boulder Libraries personnel from successful applications to the Open Access Fund.
2. CUBoulderOpenAlexAPCs_2024.csv contains data on APCs paid for articles with CU Boulder authors published in 2024 from OpenAlex (https://openalex.org/).
3. CUBoulderPublishedData2014_2024.csv contains data from the CU Boulder Faculty Reports of Professional Activities from 2014 to 2024. CU Boulder Libraries personnel coded this data for the variables provided.
4. CUBoulderTotalOAPublishing2015_2024.csv contains data on type of open access article from Unpaywall (https://unpaywall.org/) matched against data on articles authored by CU Boulder faculty from CU Boulder Elements (https://www.colorado.edu/fis/CUBE). CU Boulder Libraries personnel exported the data provided on August 15, 2025.
5. CUScholarContent20241231.csv contains data on all of the items in the CU Scholar institutional repository as of December 31, 2024. This data was exported by CU Boulder Libraries personnel from the CU Scholar (Samvera) software on December 31, 2024.
The full report based on this data can be found here: https://doi.org/10.25810/JR10-XP82
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The Association Between Relationship Distress and Health as Moderated by Optimism
Get PDFPrevious research has identified a robust association between relationship distress and healthoutcomes. Research has also identified different personality traits, such as neuroticism andconscientiousness, to be important moderators of the relationship. Researchers have not,however, studied optimism as a moderating factor in the context of relationship distress andhealth outcomes. This longitudinal study evaluated optimism as a moderator of the cross-sectional and longitudinal association between relationship distress and self-rated health in aprobability sample of American married adults. Participants were married respondents fromWave 4 and Wave 5 of the Americans’ Changing Lives (ACL) study (N = 945), who completedmeasures of relationships distress, optimism, and self-rated health at baseline and 10-yearfollow-up. Optimism and relationship distress were significantly associated with self-rated healthin cross-sectional analyses, and optimism (but not relationship distress) uniquely predicted self-rated health 10 years later, controlling for baseline self-rated health. However, optimism did notmoderate the association between relationship distress and self-rated health. Given that priorresearch has found relationship distress is a significant predictor of future health outcomes,additional research is needed examining potential moderators of this association, to identifypeople who are vulnerable and those who are resilient to the adverse impact of relationshipdistress on health.</p
Signal and Background Classification Model (BDT) Improvement & Systematics Study of J/ψ Radiative Tail and BDT Scale Factor
Get PDFThe study of Lepton Flavor Universality (LFU) has gained significant attention due
to its potential to reveal new physics (NP) beyond the Standard Model (BSM). Rare
B-meson decays, particularly the ratio R(K) between the branching fractions of B+ →
K+e+e− and B+ →K+µ+µ−, serve as key probes. While the Standard Model (SM)
predicts R(K) to be close to unity, experimental results show a deviation of up to 3.1σ
from SM expectations [1]. The analysis of R(K) involves several components, including
signal-background classification and systematic uncertainty studies. This thesis presents
improvements to a machine-learning-based classification model (Boosted Decision Tree,
BDT) for background discrimination through hyperparameter tuning. The performance
is validated using ROC and PR curves, AUC scores, and the Signal Significance method,
demonstrating an increase in signal significance from 4.88 at a BDT score cut of 3.4
(default model) to 5.61 at a cut of 3.61 (optimized model). Additionally, this thesis
evaluates two systematic uncertainties. The first arises from the J/ψ radiative tail due
to electron pair Bremsstrahlung, assessed by relaxing the lower dilepton mass (q2) window
from q2 = [2.9,3.2] GeV to q2 = [2.5,3.2] GeV, leading to a systematic uncertainty of
3.7 ±0.9%. The second concerns the BDT scale factor efficiency, accounting for residual
differences between data and Monte Carlo (MC) simulations. By varying the BDT
selection criteria, the efficiency ratio is determined to be 1.0047 ±0.0075 with a loose
BDT baseline cut (BDT>0), yielding a systematic uncertainty of 0.47 ±0.75%. These
studies contribute to a deeper understanding of R(K) measurements and the potential
violation of LFU.</p
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