Enabling scientists to understand and analyze their own experimental data by providing instruction and training in bioinformatics software, databases, analyses techniques, and emerging technologies.
This one-hour online training will provide a high-level overview of Python coding concepts, as well as some of the integrative development environments (IDEs, such as Jupyter notebooks) used for Python coding. Python is a programming language used for data science, specifically: data analysis, statistical analysis, and visualization of results. The training will feature the following IDEs: Google Colaboratory: Jupyter Notebook; and Anaconda’s: Spyder, Jupyter Notebook, and JupyterLab. This overview training will demonstrate how Read More
This one-hour online training will provide a high-level overview of Python coding concepts, as well as some of the integrative development environments (IDEs, such as Jupyter notebooks) used for Python coding. Python is a programming language used for data science, specifically: data analysis, statistical analysis, and visualization of results. The training will feature the following IDEs: Google Colaboratory: Jupyter Notebook; and Anaconda’s: Spyder, Jupyter Notebook, and JupyterLab. This overview training will demonstrate how these skills can boost productivity, rigor, and transparency in reporting research findings.
By the end of the training, attendees will be able to:
Recognize four freely available IDEs for python coding
Identify fundamental components of python code
Understand how and why notebooks support rigor and transparency in analysis
Attendees are not expected to have any prior knowledge of python coding or the IDEs to be successful in this training.
If you choose to follow along with Google Colab or Jupyter Notebooks, these IDEs should be installed and ready to go. Code will be provided during the training for this option.
This one-hour online training provides an introduction on how to sign up and access complimentary SAS training resources available to NIH and HHS employees.
By the end of this training, attendees will be able to:
Enroll in recommended SAS 9.4 trainings and courses
Navigate complimentary SAS tutorials, programming courses, and eLearning
Attendees are not expected to have any prior knowledge of SAS Read More
This one-hour online training provides an introduction on how to sign up and access complimentary SAS training resources available to NIH and HHS employees.
By the end of this training, attendees will be able to:
Enroll in recommended SAS 9.4 trainings and courses
Navigate complimentary SAS tutorials, programming courses, and eLearning
Attendees are not expected to have any prior knowledge of SAS to be successful in this training.
Presented By: Dr. Mousumi Ghosh (Office of Data Sharing ODDSS NCI), Dr. Ying Huang (Office of Data Sharing ODDSS NCI)
Organized by
NCI Office of Data Sharing
Description
Join us as poster presenters from the ODS Symposium showcase innovative work that highlights powerful resources, inspiring examples, and the far-reaching impact of data sharing and reuse.
Moderators: Ying Huang and Mousumi Ghosh
Join us as poster presenters from the ODS Symposium showcase innovative work that highlights powerful resources, inspiring examples, and the far-reaching impact of data sharing and reuse.
Partek Flow enables scientists to construct analysis workflows for multi-omics sequencing data including DNA, bulk and single cell RNA, spatial transcriptomics, ATAC and ChIP. It is a point-and-click software suitable for those who wish to avoid the steep learning curve involved with analyzing sequencing data through coding. In this class, taught by Partek scientist, participants will learn about conducting QA/QC, performing cell type classification, obtaining differential analysis results, performing pathway analysis, and creating Read More
Partek Flow enables scientists to construct analysis workflows for multi-omics sequencing data including DNA, bulk and single cell RNA, spatial transcriptomics, ATAC and ChIP. It is a point-and-click software suitable for those who wish to avoid the steep learning curve involved with analyzing sequencing data through coding. In this class, taught by Partek scientist, participants will learn about conducting QA/QC, performing cell type classification, obtaining differential analysis results, performing pathway analysis, and creating visualizations for single cell RNA sequencing data. This session is a demonstration and not hands-on. Experience using or access to Partek Flow is not needed for participation. Attendance is restricted to NIH staff.
Presented By: Ajay Wasan (University of Pittsburgh)
Organized by
NIH Pain SIG
Description
This talk will focus on analyses of the Patient Outcomes Repository for Treatment (PORT) which is a large registry of chronic pain treatment outcomes from patients seen in the pain clinics at the University Pittsburgh Medical Center (UPMC). Using methods such as propensity scoring, stratified modeling, and supervised machine learning, we can determine which treatments for chronic pain are or are not effective, the phenotypes most responsive to each treatment, and predict which treatments Read More
This talk will focus on analyses of the Patient Outcomes Repository for Treatment (PORT) which is a large registry of chronic pain treatment outcomes from patients seen in the pain clinics at the University Pittsburgh Medical Center (UPMC). Using methods such as propensity scoring, stratified modeling, and supervised machine learning, we can determine which treatments for chronic pain are or are not effective, the phenotypes most responsive to each treatment, and predict which treatments will be most effective in any new patient based on their phenotype (such as medications, injections, physical therapy, or mental health care).
This one and a half-hour online training covers the basic principles of FAIR (Findable, Accessible, Interoperable, Reusable) data and why it is important to make your data FAIR.
By the end of this training, attendees will be able to:
Define FAIR data
Read More
This one and a half-hour online training covers the basic principles of FAIR (Findable, Accessible, Interoperable, Reusable) data and why it is important to make your data FAIR.
By the end of this training, attendees will be able to:
Define FAIR data
Explain what purpose FAIR data serves
Apply FAIR data principles to make data findable, accessible, interoperable, and reusable
In this lesson, attendees will continue learning how to create publishable figures with ggplot2. Topics will include statistical transformations, coordinate systems, and themes.
In this lesson, attendees will continue learning how to create publishable figures with ggplot2. Topics will include statistical transformations, coordinate systems, and themes.
Scikit-learn is a free and open-source Python library for machine learning. It is built on top of other fundamental Python libraries like NumPy, SciPy, and Matplotlib. Users will be introduced to scikit-learn and its usage, followed by the basic Machine Line pipeline and a simple Classification example using scikit-learn on a publicly available Diabetes dataset.
Scikit-learn is a free and open-source Python library for machine learning. It is built on top of other fundamental Python libraries like NumPy, SciPy, and Matplotlib. Users will be introduced to scikit-learn and its usage, followed by the basic Machine Line pipeline and a simple Classification example using scikit-learn on a publicly available Diabetes dataset.
This lesson introduces general recommendations and tips to consider when creating effective and reproducible visualizations. Additional topics to be discussed include multi-figure panels, complementary or related R packages, and the use of ggplot2 in functions.
This lesson introduces general recommendations and tips to consider when creating effective and reproducible visualizations. Additional topics to be discussed include multi-figure panels, complementary or related R packages, and the use of ggplot2 in functions.
