Preregistration, the act of specifying a research plan in advance, is becoming …
Preregistration, the act of specifying a research plan in advance, is becoming a central step in the way science is conducted. Preregistration for infant researchers might be different than in other fields, due to the specific challenges having to do with testing infants. Infants are a hard-to-reach population, usually yielding small sample sizes, they have a low attention span which usually can limit the number of trials, and they can be excluded based on hard to predict complications (e.g., parental interference, fussiness). In addition, as effects themselves potentially change with age and population, it is hard to calculate an a priori effect size. At the same time, these very factors make preregistration in infant studies a valuable tool. A priori examination of the planned study, including the hypotheses, sample size, and resulting statistical power, increase the credibility of single studies and thus add value to the field. It might arguably also improve explicit decision-making to create better studies. We present an in-depth discussion of the issues uniquely relevant to infant researchers, and ways to contend with them in preregistration and study planning. We provide recommendations to researchers interested in following current best practices.
This resource is a video abstract of a research paper created by …
This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:
"Babies born before 37 weeks often need antibiotics to stave off infection. Antibiotic exposure like this can increase the amount of antibiotic resistance genes carried by the microbes in their gut. But giving them a probiotic of beneficial bacteria may help. To test this, researchers examined the microbiome antibiotic resistance genes in three groups of infants: preterm infants with probiotic supplementation, preterm infants without probiotic supplementation, and full-term infants. The samples were collected from the preterm infants near their predicted due date and from the full-term infants when they were 10 days old. Overall, the number of antibiotic resistance genes didn’t differ between groups, but the types and resistance mechanisms did. The preterm infants not given probiotics had over 80 antibiotic resistance genes unique to their group and had more genes associated with antibiotic inactivation mechanisms than the other groups..."
The rest of the transcript, along with a link to the research itself, is available on the resource itself.
Context: Conducting experiments is central to research machine learning research to benchmark, …
Context: Conducting experiments is central to research machine learning research to benchmark, evaluate and compare learning algorithms. Consequently it is important we conduct reliable, trustworthy experiments. Objective: We investigate the incidence of errors in a sample of machine learning experiments in the domain of software defect prediction. Our focus is simple arithmetical and statistical errors. Method: We analyse 49 papers describing 2456 individual experimental results from a previously undertaken systematic review comparing supervised and unsupervised defect prediction classifiers. We extract the confusion matrices and test for relevant constraints, e.g., the marginal probabilities must sum to one. We also check for multiple statistical significance testing errors. Results: We find that a total of 22 out of 49 papers contain demonstrable errors. Of these 7 were statistical and 16 related to confusion matrix inconsistency (one paper contained both classes of error). Conclusions: Whilst some errors may be of a relatively trivial nature, e.g., transcription errors their presence does not engender confidence. We strongly urge researchers to follow open science principles so errors can be more easily be detected and corrected, thus as a community reduce this worryingly high error rate with our computational experiments.
Short Description: Now available in print at Amazon.com and via the OSU …
Short Description: Now available in print at Amazon.com and via the OSU Press! Data Dashboard
Long Description: A Primer for Computational Biology aims to provide life scientists and students the skills necessary for research in a data-rich world. The text covers accessing and using remote servers via the command-line, writing programs and pipelines for data analysis, and provides useful vocabulary for interdisciplinary work. The book is broken into three parts: Introduction to Unix/Linux: The command-line is the “natural environment” of scientific computing, and this part covers a wide range of topics, including logging in, working with files and directories, installing programs and writing scripts, and the powerful “pipe” operator for file and data manipulation. Programming in Python: Python is both a premier language for learning and a common choice in scientific software development. This part covers the basic concepts in programming (data types, if-statements and loops, functions) via examples of DNA-sequence analysis. This part also covers more complex subjects in software development such as objects and classes, modules, and APIs. Programming in R: The R language specializes in statistical data analysis, and is also quite useful for visualizing large datasets. This third part covers the basics of R as a programming language (data types, if-statements, functions, loops and when to use them) as well as techniques for large-scale, multi-test analyses. Other topics include S3 classes and data visualization with ggplot2.
Word Count: 111597
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The purpose of this study was to determine if principal tenure, principal …
The purpose of this study was to determine if principal tenure, principal stability, and principal educational experience in public education along with school-level variables predicted elementary school student achievement. A second purpose was to examine whether there was a significant difference between (a) levels of principal tenure and levels of principal educational experience on elementary school student achievement and (b) levels of principal stability and levels of principal educational experience on elementary school student achievement. The findings revealed that the school-level variables were stronger predictors of student achievement than principal-level variables. However, for both grade 3 and grade 5, principal tenure was a significant predictor across subject areas tested. As the length of a principal’s tenure at a school increased, the schools mean scale scores increased. Findings also revealed that schools with greater principal stability had higher school mean scale scores. In addition, principal educational experience had less of an impact on student achievement than principal tenure or principal stability.
By the end of this section, you will be able to: Analyze …
By the end of this section, you will be able to:
Analyze how price elasticities impact revenue Evaluate how elasticity can cause shifts in demand and supply Predict how the long-run and short-run impacts of elasticity affect equilibrium Explain how the elasticity of demand and supply determine the incidence of a tax on buyers and sellers
By the end of this section, you will be able to: Predict …
By the end of this section, you will be able to:
Predict shifts in the demand and supply curves of the labor market Explain the impact of new technology on the demand and supply curves of the labor market Explain price floors in the labor market such as minimum wage or a living wage
By the end of this section, you will be able to: Explain …
By the end of this section, you will be able to:
Explain the determinants of trade and current account balance Identify and calculate supply and demand for financial capital Explain how a nation's own level of domestic saving and investment determines a nation's balance of trade Predict the rising and falling of trade deficits based on a nation's saving and investment identity
This course studies basic optimization and the principles of optimal control. It …
This course studies basic optimization and the principles of optimal control. It considers deterministic and stochastic problems for both discrete and continuous systems. The course covers solution methods including numerical search algorithms, model predictive control, dynamic programming, variational calculus, and approaches based on Pontryagin's maximum principle, and it includes many examples and applications of the theory.
CK-12 Foundation's new and improved Advanced Probability and Statistics-Second Edition FlexBook introduces …
CK-12 Foundation's new and improved Advanced Probability and Statistics-Second Edition FlexBook introduces students to basic topics in statistics and probability, but finishes with the rigorous topics an advanced placement course requires.
CK-12 Advanced Probability and Statistics introduces students to basic topics in statistics …
CK-12 Advanced Probability and Statistics introduces students to basic topics in statistics and probability but finishes with the rigorous topics an advanced placement course requires. Includes visualizations of data, introduction to probability, discrete probability distribution, normal distribution, planning and conducting a study, sampling distributions, hypothesis testing, regression and correlation, Chi-Square, analysis of variance, and non-parametric statistics.
CK-12 Foundation's Basic Probability and Statistics - A Short Course is an …
CK-12 Foundation's Basic Probability and Statistics - A Short Course is an introduction to theoretical probability and data organization. Students learn about events, conditions, random variables, and graphs and tables that allow them to manage data.
This resource is a video abstract of a research paper created by …
This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:
"A new preclinical animal model study has identified biomarker panels that might be useful predictors and indicators of outcomes after meniscus allograft transplantation, or MAT. These biomarkers may enable real-time assessment of graft survival after surgery. The findings are published in The American Journal of Sports Medicine. Meniscal tears and degeneration—or partial meniscectomy surgeries intended to alleviate these issues—can cause meniscal deficiency. In meniscal deficiency, lack of a complete meniscus increases stress to the knee joint, contributing to pain, dysfunction, and osteoarthritis. Restoring the meniscus through strategies like MAT can prevent these issues. However, there are currently no methods for predicting and evaluating MAT graft success or failure in real time. To help develop such a method, researchers searched for biomarkers of MAT outcomes in a dog model. First, they induced meniscal deficiency in dogs through arthroscopic medial meniscal release surgery..."
The rest of the transcript, along with a link to the research itself, is available on the resource itself.
Data Carpentry Genomics workshop lesson to learn how to structure your metadata, …
Data Carpentry Genomics workshop lesson to learn how to structure your metadata, organize and document your genomics data and bioinformatics workflow, and access data on the NCBI sequence read archive (SRA) database. Good data organization is the foundation of any research project. It not only sets you up well for an analysis, but it also makes it easier to come back to the project later and share with collaborators, including your most important collaborator - future you. Organizing a project that includes sequencing involves many components. There’s the experimental setup and conditions metadata, measurements of experimental parameters, sequencing preparation and sample information, the sequences themselves and the files and workflow of any bioinformatics analysis. So much of the information of a sequencing project is digital, and we need to keep track of our digital records in the same way we have a lab notebook and sample freezer. In this lesson, we’ll go through the project organization and documentation that will make an efficient bioinformatics workflow possible. Not only will this make you a more effective bioinformatics researcher, it also prepares your data and project for publication, as grant agencies and publishers increasingly require this information. In this lesson, we’ll be using data from a study of experimental evolution using E. coli. More information about this dataset is available here. In this study there are several types of files: Spreadsheet data from the experiment that tracks the strains and their phenotype over time Spreadsheet data with information on the samples that were sequenced - the names of the samples, how they were prepared and the sequencing conditions The sequence data Throughout the analysis, we’ll also generate files from the steps in the bioinformatics pipeline and documentation on the tools and parameters that we used. In this lesson you will learn: How to structure your metadata, tabular data and information about the experiment. The metadata is the information about the experiment and the samples you’re sequencing. How to prepare for, understand, organize and store the sequencing data that comes back from the sequencing center How to access and download publicly available data that may need to be used in your bioinformatics analysis The concepts of organizing the files and documenting the workflow of your bioinformatics analysis
This course will provide an introduction to protein classification and basic concepts, …
This course will provide an introduction to protein classification and basic concepts, such as proteins families, domains and sequence features.
By the end of the course you will be able to: Describe the importance of classifying proteins Explain how protein families, domains and sequence features can be defined, and how these can be used to classify proteins Describe the different predictive methods you can use to help classify proteins: patterns, profiles, fingerprints and hidden Markov models (HMMs) List which resources for classifying proteins according to family, domain and sequence features are available at the EMBL-EBI
This resource is a video abstract of a research paper created by …
This resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:
"Artificial intelligence is making rapid advances in medicine. Already, there are machine learning algorithms that can outperform doctors in some medical fields. There’s only one fairly big problem: experts aren’t quite sure how these algorithms work. While designers know full well what goes into the A-I systems they build and what comes out, the learning part in between is often too complex to comprehend. To their users, machine learning algorithms are effectively black boxes. Now, researchers from the RIKEN Center for Advanced Intelligence Project in Japan are lifting the lid. They’ve developed a deep-learning system that can outperform human experts in predicting whether prostate cancer will reoccur within one year. More importantly, the deep learning system they developed can acquire human-understandable features from unannotated pathology images to offer up critical clues that could help humans make better diagnoses themselves..."
The rest of the transcript, along with a link to the research itself, is available on the resource itself.
Policies that mandate public data archiving (PDA) successfully increase accessibility to data …
Policies that mandate public data archiving (PDA) successfully increase accessibility to data underlying scientific publications. However, is the data quality sufficient to allow reuse and reanalysis? We surveyed 100 datasets associated with nonmolecular studies in journals that commonly publish ecological and evolutionary research and have a strong PDA policy. Out of these datasets, 56% were incomplete, and 64% were archived in a way that partially or entirely prevented reuse. We suggest that cultural shifts facilitating clearer benefits to authors are necessary to achieve high-quality PDA and highlight key guidelines to help authors increase their data’s reuse potential and compliance with journal data policies.
SYNOPSIS: In this lesson, students learn about extreme weather, create an infographic, …
SYNOPSIS: In this lesson, students learn about extreme weather, create an infographic, and educate others on the knowledge gained from this unit.
SCIENTIST NOTES: This lesson allows students to understand the difference between weather and climate, the important variables that cause changes in weather, how weather and climate are predicted, the impact of weather extremes on the climate, and how human activities have accelerated wildfires, disrupted the water cycle, and caused other erratic weather disturbances in their communities. They would be able to explore which weather events are frequent and the overall combined impacts. All materials were rigorously reviewed, and this lesson has passed our science credibility process.
POSITIVES: -Students participate in multiple interactive and hands-on learning activities to engage in kinesthetic, auditory, and visual learning. -Students have an opportunity to share their growth and knowledge throughout the unit with other students and community members.
ADDITIONAL PREREQUISITES: -This is lesson 4 of 4 in our 6th-8th grade Water Cycle, Deforestation, and Climate Change unit. -Teachers need to determine how to choose the best course of action for sharing student learning. Options include the following: -Class vote -Teacher predetermines based on their best judgment -Student panel is created -Different groups choose different courses of action
DIFFERENTIATION: -Students may use the Emotions Board for vocabulary support as they watch the videos in the Inquire section -At the end of the unit, a classroom gallery walk is recommended. -Some ideas for extensions at the end of the unit include: -Inviting other classes in for a gallery walk -Hosting a community night where community members can be educated on what students have learned in the unit -Mailing student materials to different leaders in the community, particularly leaders that are in charge of the local water sources
Students learn about the concept of pushing, as well as the relationship …
Students learn about the concept of pushing, as well as the relationship between force and mass. Students practice measurement skills using pan scales and rulers to make predictions about mass and distance. A LEGO MINDSTORMS(TM) NXT robot is used to test their hypotheses. By the end of the activity, students have a better understanding of robotics, mass and friction and the concept of predicting.
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