25 research outputs found
SchlossLab/mikropml: mikropml 1.1.1
No full textFixed bugs related to grouping correlated features (#276, @kelly-sovacool).
Also, group_correlated_features() is now a user-facing function
UMCarpentries/intro-curriculum-r: v1.0.0
No full textThe paper describing this curriculum will soon be published in JOSE!
What's Changed
Add instructions to create GitHub PAT by @MrFlick in https://github.com/UMCarpentries/intro-curriculum-r/pull/145
Typos and other minor corrections by @kdillmcfarland in https://github.com/UMCarpentries/intro-curriculum-r/pull/151
Update setup.md by @kdillmcfarland in https://github.com/UMCarpentries/intro-curriculum-r/pull/146
Include overview summary on index page by @zenalapp in https://github.com/UMCarpentries/intro-curriculum-r/pull/153
Update install instructions by @zenalapp in https://github.com/UMCarpentries/intro-curriculum-r/pull/155
Fix broken links by @kelly-sovacool in https://github.com/UMCarpentries/intro-curriculum-r/pull/156
Update GitHub Actions & related code from the carpentries/styles repo by @kelly-sovacool in https://github.com/UMCarpentries/intro-curriculum-r/pull/157
JOSE submission suggestions by @zenalapp in https://github.com/UMCarpentries/intro-curriculum-r/pull/159
Incorporate JOSE feedback in Git/GitHub lesson by @kelly-sovacool in https://github.com/UMCarpentries/intro-curriculum-r/pull/158
Use the carpentries/styles website build workflow by @kelly-sovacool in https://github.com/UMCarpentries/intro-curriculum-r/pull/160
Minor edits by @zenalapp in https://github.com/UMCarpentries/intro-curriculum-r/pull/163
Fix callout boxes & comment-out glossary in Git/GitHub by @kelly-sovacool in https://github.com/UMCarpentries/intro-curriculum-r/pull/164
Fix formatting in R for Plotting by @kelly-sovacool in https://github.com/UMCarpentries/intro-curriculum-r/pull/162
Beautify the README with badges by @kelly-sovacool in https://github.com/UMCarpentries/intro-curriculum-r/pull/165
Auto-detect broken links with GitHub Actions by @kelly-sovacool in https://github.com/UMCarpentries/intro-curriculum-r/pull/154
Better broken link detection by @MrFlick in https://github.com/UMCarpentries/intro-curriculum-r/pull/166
Suggesting changes to CoC guidelines language by @tracykteal in https://github.com/UMCarpentries/intro-curriculum-r/pull/169
Clarify our connection to The Carpentries by @kelly-sovacool in https://github.com/UMCarpentries/intro-curriculum-r/pull/171
New Contributors
@kdillmcfarland made their first contribution in https://github.com/UMCarpentries/intro-curriculum-r/pull/151
@tracykteal made their first contribution in https://github.com/UMCarpentries/intro-curriculum-r/pull/169
Full Changelog: https://github.com/UMCarpentries/intro-curriculum-r/compare/v0.1.0...v1.0.
SchlossLab/mikropml: mikropml 1.2.0
No full textNew parameter cross_val added to run_ml() allows users to define their own custom cross-validation scheme (#278, @kelly-sovacool).
Also added a new parameter calculate_performance, which controls whether performance metrics are calculated (default: TRUE). Users may wish to skip performance calculations when training models with no cross-validation.
New parameter group_partitions added to run_ml() allows users to control which groups should go to which partition of the train/test split (#281, @kelly-sovacool).
Modified the training_frac parameter in run_ml() (#281, @kelly-sovacool).
By default, training_frac is a fraction between 0 and 1 that specifies how much of the dataset should be used in the training fraction of the train/test split.
Users can instead give training_frac a vector of indices that correspond to which rows of the dataset should go in the training fraction of the train/test split. This gives users direct control over exactly which observations are in the training fraction if desired
SchlossLab/Barron_IBD-CDI_2022: 1.0.0 mBio paper
No full textThe paper has now been published in mBio: https://doi.org/10.1128/mbio.01904-2
SchlossLab/Sovacool_OptiFit_mSphere_2022: OptiFit paper 1.0.0
No full textThe paper accompanying the OptiFit algorithm is now out in mSphere! https://journals.asm.org/doi/10.1128/msphere.00916-21
Assigning amplicon sequences to operational taxonomic units (OTUs) is an important step in characterizing microbial communities across large data sets. A notable difference between de novo clustering and database-dependent reference clustering methods is that OTU assignments from de novo methods may change when new sequences are added. However, one may wish to incorporate new samples to previously clustered data sets without clustering all sequences again, such as when comparing across data sets or deploying machine learning models. Existing reference-based methods produce consistent OTUs but only consider the similarity of each query sequence to a single reference sequence in an OTU, resulting in assignments that are worse than those generated by de novo methods. To provide an efficient method to fit sequences to existing OTUs, we developed the OptiFit algorithm. Inspired by the de novo OptiClust algorithm, OptiFit considers the similarity of all pairs of reference and query sequences to produce OTUs of the best possible quality. We tested OptiFit using four data sets with two strategies: (i) clustering to a reference database and (ii) splitting the data set into a reference and query set, clustering the references using OptiClust, and then clustering the queries to the references. The result is an improved implementation of reference-based clustering. OptiFit produces OTUs of a quality similar to that of OptiClust at faster speeds when using the split data set strategy. OptiFit provides a suitable option for users requiring consistent OTU assignments at the same quality as afforded by de novo clustering methods
Software accompanying the manuscript: Predicting Severity of C. difficile Infections from the Taxonomic Composition of the Gut Microbiome
No full textThis version is included as a chapter in my dissertation
Still in the “Drivers’ Seat”, But for How Long? ASEAN’s Capacity for Leadership in East-Asian International Relations
No full textThis paper assesses the capacity of the Association of Southeast Asian Nations (ASEAN) to moderate great-power relations in East Asia, especially in light of recent regional developments that have challenged ASEAN’s traditional modus operandi and its corporate cohesion. The first of three sections argues that capacity emerges not from institutional arrangements but rather the social relationships that give rise to particular institutions, and therefore can only be understood relationally. A number of key relationships are highlighted and explored in the rest of the paper. First, the relationships among regional great powers, which are considered in section two. Second, the relationships among ASEAN states, and between ASEAN states and their own societies, which are considered in section three. The paper's basic argument is that the first set of relationships is essentially what gives ASEAN its capacity to play a wider regional role. However, it also sets profound constraints for what this role can involve in practical terms. The second set of relationships also creates serious and deep constraints that are often not well understood. However, despite the serious limitations on ASEAN’s leadership role, unless the first set of relationships change, this role is likely to continue, regardless of how frustrating or ineffectual it might be
Assessment of the impacts under future climate change on the energy systems with the POLES model
No full textThis paper presents the way we try to explore the most important impacts of climate change on the energy systems with the POLES model. We present the main features and adaptations of the POLES model with details on the treatment of the electricity demand in the residential and service sector, of the hydro and thermal electricity generation and energy demand for water supply while using climate drivers coming from other models. Comparisons of the results of the Reference projection with and without the taking into account of the effects of climate change on energy systems for the World and for Europe (EU27) up to 2100 are displayed in the paper.POLES MODEL ; CLIMATE CHANGE ; SCENARIO ; ENERGY SYSTEM ; ELECTRICITY PRODUCTION
J Open Source Educ
No full textWe are bioinformatics trainees at the University of Michigan who started a local chapter of Girls Who Code to provide a fun and supportive environment for high school women to learn the power of coding. Our goal was to cover basic coding topics and data science concepts through live coding and hands-on practice. However, we could not find a resource that exactly met our needs. Therefore, over the past three years, we have developed a curriculum and instructional format using Jupyter notebooks to effectively teach introductory Python for data science. This method, inspired by The Carpentries organization, uses bite-sized lessons followed by independent practice time to reinforce coding concepts, and culminates in a data science capstone project using real-world data. We believe our open curriculum is a valuable resource to the wider education community and hope that educators will use and improve our lessons, practice problems, and teaching best practices. Anyone can contribute to our Open Educational Resources on GitHub.F31 LM012946/LM/NLM NIH HHSUnited States/T32 AI007528/AI/NIAID NIH HHSUnited States/T42 OH008455/OH/NIOSH CDC HHSUnited States/T32 DA007281/DA/NIDA NIH HHSUnited States/T32 HG000040/HG/NHGRI NIH HHSUnited States/T32 GM070449/GM/NIGMS NIH HHSUnited States/R01 ES028802/ES/NIEHS NIH HHSUnited States/T32 NS076401/NS/NINDS NIH HHSUnited States/T32 CA140044/CA/NCI NIH HHSUnited States
What do we know about UK household adaptation to climate change? A systematic review
No full textThe UK Government’s first National Adaptation Programme seeks to create a ‘climate-ready society’ capable of making well-informed and far-sighted decisions to address risks and opportunities posed by a changing climate, where individual households are expected to adapt when it is in their interest to do so. How, and to what extent, households are able to do this remains unclear. Like other developed countries, research on UK adaptation has focused predominately on public and private organisations. To fill that gap, a systematic literature review was conducted to understand what actions UK households have taken in response to, or in anticipation of, a changing climate; what drives or impedes these actions; and whether households will act autonomously. We found that UK households struggle to build long-term adaptive capacity and are reliant upon traditional reactive coping responses. Of concern is that these coping responses are less effective for some climate risks (e.g. flooding); cost more over the long-term; and fail to create household capacity to adapt to other stresses. While low-cost, low-skill coping responses were already being implemented, the adoption of more permanent physical measures, behavioural changes, and acceptance of new responsibilities are unlikely to happen autonomously without further financial or government support. If public policy on household adaptation to climate change is to be better informed than more high-quality empirical research is urgently needed
