In this webinar Professor Brian Nosek, Executive Director of the Center for Open Science (https://cos.io), outlines the practice of Preregistration and how it can aid in increasing the rigor and reproducibility of research. The webinar is co-hosted by the Health Research Alliance, a collaborative member organization of nonprofit research funders. Slides available at: https://osf.io/9m6tx/

The best way to learn how to program is to do something useful, so this introduction to MATLAB is built around a common scientific task: data analysis. Our real goal isn’t to teach you MATLAB, but to teach you the basic concepts that all programming depends on. We use MATLAB in our lessons because: we have to use something for examples; it’s well-documented; it has a large (and growing) user base among scientists in academia and industry; and it has a large library of packages available for performing diverse tasks. But the two most important things are to use whatever language your colleagues are using, so that you can share your work with them easily, and to use that language well.

The best way to learn how to program is to do something useful, so this introduction to Python is built around a common scientific task: data analysis. Arthritis Inflammation We are studying inflammation in patients who have been given a new treatment for arthritis, and need to analyze the first dozen data sets of their daily inflammation. The data sets are stored in comma-separated values (CSV) format: each row holds information for a single patient, columns represent successive days. The first three rows of our first file look like this: 0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0 0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1 0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1 Each number represents the number of inflammation bouts that a particular patient experienced on a given day. For example, value “6” at row 3 column 7 of the data set above means that the third patient was experiencing inflammation six times on the seventh day of the clinical study. So, we want to: Calculate the average inflammation per day across all patients. Plot the result to discuss and share with colleagues. To do all that, we’ll have to learn a little bit about programming.

The best way to learn how to program is to do something useful, so this introduction to R is built around a common scientific task: data analysis. Our real goal isn’t to teach you R, but to teach you the basic concepts that all programming depends on. We use R in our lessons because: we have to use something for examples; it’s free, well-documented, and runs almost everywhere; it has a large (and growing) user base among scientists; and it has a large library of external packages available for performing diverse tasks. But the two most important things are to use whatever language your colleagues are using, so you can share your work with them easily, and to use that language well. We are studying inflammation in patients who have been given a new treatment for arthritis, and need to analyze the first dozen data sets of their daily inflammation. The data sets are stored in CSV format (comma-separated values): each row holds information for a single patient, and the columns represent successive days. The first few rows of our first file look like this: 0,0,1,3,1,2,4,7,8,3,3,3,10,5,7,4,7,7,12,18,6,13,11,11,7,7,4,6,8,8,4,4,5,7,3,4,2,3,0,0 0,1,2,1,2,1,3,2,2,6,10,11,5,9,4,4,7,16,8,6,18,4,12,5,12,7,11,5,11,3,3,5,4,4,5,5,1,1,0,1 0,1,1,3,3,2,6,2,5,9,5,7,4,5,4,15,5,11,9,10,19,14,12,17,7,12,11,7,4,2,10,5,4,2,2,3,2,2,1,1 0,0,2,0,4,2,2,1,6,7,10,7,9,13,8,8,15,10,10,7,17,4,4,7,6,15,6,4,9,11,3,5,6,3,3,4,2,3,2,1 0,1,1,3,3,1,3,5,2,4,4,7,6,5,3,10,8,10,6,17,9,14,9,7,13,9,12,6,7,7,9,6,3,2,2,4,2,0,1,1 We want to: load that data into memory, calculate the average inflammation per day across all patients, and plot the result. To do all that, we’ll have to learn a little bit about programming.

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

Python is a general purpose programming language that is useful for writing scripts to work effectively and reproducibly with data. This is an introduction to Python designed for participants with no programming experience. These lessons can be taught in a day (~ 6 hours). They start with some basic information about Python syntax, the Jupyter notebook interface, and move through how to import CSV files, using the pandas package to work with data frames, how to calculate summary information from a data frame, and a brief introduction to plotting. The last lesson demonstrates how to work with databases directly from Python.

Póngase Critical Equipo STEM. El Centro de Extensión y Educación en Ciencias Naturales colabora con la facultad de CSU, los Parques Nacionales y los programas de ciencia ciudadana para traducir su investigación científica actual en experiencias STEM únicas para los estudiantes en forma de kits educativos que se pueden prestar. Cada kit contiene casi todos los materiales necesarios (menos cosas comunes como agua y toallas de papel) para explorar algunos temas de investigación científica realmente interesantes. enviando un formulario de recogida local o un formulario de entrega disponible en el sitio web vinculado. Utilice la información de contacto en la página de descripción general del kit STEM para obtener más información. https://www.cns-eoc.colostate.edu/stem-kits/ Este kit se proporciona de forma gratuita para uso educativo.

"Quizizz is a free tool. It works on any device: web browser, iOS, Android and Chrome apps. You can access hundreds of ready-made learning quizzes or create your own. Join as a teacher, pick a quiz, and use the code for a virtual room to give to your students."

The way you make a quiz, you host it and then you see them playing it on their mobile phones/tablets or laptops with the Scoreboard Seen Live on the Screen/Projector in the Class makes it a real happy classroom. The extra-edge comes with the Music that is being played by the App or the Website during the quiz.

The additional features about creating a quiz are more beneficial and good with the Quizizz. You can download an excel sheet from Quizizz and enter the questions as guided in this format. While using the desktop version, you can upload this .csv and the quiz is generated. Another excellent feature is to TELEPORT the questions from Pre-made quizzes. Click on teleport after cresting name of the quiz. On the right side of the screen you see that there are many quizzes opened and you can add a question to your quiz, just by clicking ADD.

Groups of 3-4 students work in parallel on different radiocarbon data sets in this project assignment; the content described here would comprise one group data set including a brief descriptive statement and bibliographic reference, raw data including errors, background lecture notes, and an example final poster presentation.

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Students answer several questions about Rayleigh fractionation and the oxygen isotope composition of ice cores which require them to manipulate the values and equations in a MS Excel spreadsheet. This helps them to see how the oxygen isotope composition of ice cores changes with temperature in the high latitudes. Future iterations of the exercise should include a visualization of the Clausius-Clapeyron equation.

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Really Ancient Fossils STEM Kit. The Natural Sciences Education & Outreach Center collaborates with CSU faculty, National Parks and citizen science programs to translate their current scientific research into unique STEM experiences for students in the form of Educational Kits that can be checked out. Each kit contains just about all of the materials needed (minus common things like water and paper towels) to explore some really interesting scientific research topics.The kits are available for teachers and informal educators in Colorado to check out for a duration of a week by submitting either a local pickup form or a delivery form available at the linked website. Due to the extreme weight of the sand used in this Kit, local pickup is the only option. This kit is provided free for educational use. This kit is also available in Spanish.

This activity is a digital student workbook that compliments the "Recognizing Patterns in Earth's Climate History" Lesson. Students will make observations and note patterns they see in sediment cores. Students will make inferences about Earth's climate history based on their observations. This workbook is on Google Slides and can be utilized in a Learning Management System.

Provenance: Molly Ludwick, Kings Mountain Middle School
Reuse: This item is offered under a Creative Commons Attribution-NonCommercial-ShareAlike license http://creativecommons.org/licenses/by-nc-sa/3.0/ You may reuse this item for non-commercial purposes as long as you provide attribution and offer any derivative works under a similar license.

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This Reflection Toolkit, compiled by the faculty inquiry group (FIG), includes classroom strategies for integrating reflection into one's existing syllabi. The lesson plans highlight how to encourage effective student reflections.The toolkit includes best practices to facilitate reflection in classes across the disciplines in the context of a variety of student-centered activities (including group-work, online learning, and interactive modules).

A guided activity for students to explore the relationship between climate and plant and animal distributions in the past, present, and future. Students use the Neotoma Paleoecology Database, USDA Climate Change Tree Atlas, USGS Atlas of Relations Between Climatic Parameters and Distributions of Important Trees and Shrubs in North America (Professional Paper 1650 A/B), and climate model output.

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Scientific code is different from production software. Scientific code, by producing results that are then analyzed and interpreted, participates in the elaboration of scientific conclusions. This imposes specific constraints on the code that are often overlooked in practice. We articulate, with a small example, five characteristics that a scientific code in computational science should possess: re-runnable, repeatable, reproducible, reusable and replicable. The code should be executable (re-runnable) and produce the same result more than once (repeatable); it should allow an investigator to reobtain the published results (reproducible) while being easy to use, understand and modify (reusable), and it should act as an available reference for any ambiguity in the algorithmic descriptions of the article (replicable).

The Research Data Management Academy (RDMLA) is a global, free online professional development program for librarians, information professionals, or other professionals who work in a research-intensive environment. The curriculum focuses on the knowledge and skills needed to collaborate with researchers and other stakeholders on data management. RDMLA features a unique partnership between a library and information science academic program, academic health sciences and research libraries, and industry publisher. All of the content is hosted on Canvas Network, freely available, and open for reuse under a CC-BY-NC-SA license.

This module dwells on a number of methods (including old and new) available for research evaluation. The module comprises the following four units:
Unit 1. Introduction to Research Evaluation Metrics and Related Indicators.
Unit 2. Innovations in Measuring Science and Scholarship: Analytical Tools and Indicators in Evaluation Scholarship Communications.
Unit 3. Article and Author Level Measurements, and
Unit 4. Online Citation and Reference Management Tools.
Brief overviews of the units are presented below.
Unit 1 encompassed and discussed citation analysis, use of citation-based indicators for research evaluation, common bibliometric indicators, classical bibliometric laws, author level indicators using authors' public profiles, article level metrics using altmetric tools. It is to be noted that author level indicators and article level metrics are new tools for research evaluation. Author level indicators encompasses h index, citations count, i10 index, g index, articles with citation, average citations per article, Eigenfactor score, impact points, and RG score. Article level metrics or altmetrics are based on Twitter, Facebook, Mendeley, CiteULike, and Delicious which have been discussed. All technical terms used in the Unit have been defined.
Unit 2 deals with analytical tools and indicators used in evaluating scholarly communications. The tools covered are The Web of Science, Scopus, Indian Citation Index (ICI), CiteSeerX, Google Scholar and Google Scholar Citations. Among these all the tools except Indian Citation Index (ICI) are international in scope. ICI is not very much known outside India. It is a powerful tool as far Indian scholarly literature is concerned. As Indian journals publish a sizable amount of foreign literature, the tool will be useful for foreign countries as well. The analytical products with journal performance metrics Journal Citation Reports (JCR®) has also been described. In the chapter titled New Platforms for Evaluating Scholarly Communications three websites i.e. SCImago Journal & Country Rank (SJR) [ScimagoJR.com], eigenFACTOR.org, JournalMetrics.com and one software called Publish or Perish (POP) Software have been discussed.
Article and author level measurements have been discussed in Unit 3. Author and researcher identifiers are absolutely essential for searching databases in the WWW because a name like D Singh can harbour a number of names such as Dan Singh, Dhan Singh, Dhyan Singh, Darbara Singh, Daulat Singh, Durlabh Singh and more. The ResearcherID.com, launched by Thomson Reuters, is a web-based global registry of authors and researchers that individualises each and every name. Open Researcher and Contributor ID (ORCID) is also a registry that uniquely identifies an author or researcher. Both have been discussed in this Unit. Article Level Metrics (Altmetrics) has been treated in this Unit with the discussion as to how altmetrics can be measured with Altmetric.com and ImpactStory.org. Altmetrics for Online Journals has also been touched. There are a number of academic social networks of which ResearchGate.net, Academia.edu, GetCited.org, etc. have been discussed. Regional journal networks with bibliometric indicators are also in existence. Two networks of this type such as SciELO – Scientific Electronic Library Online, and Redalyc have been dealt with.
The last unit (Unit 4) is on online citation and reference management tools. The tools discussed are Mendeley, CiteULike, Zotero, Google Scholar Library, and EndNote Basic. The features of all the management tools have been discussed with figures, tables, and text boxes.
This is Module Four of the UNESCO's Open Access Curriculum for Researchers.
Full-Text is available at http://unesdoc.unesco.org/images/0023/002322/232210E.pdf

A short online tutorial to introduce readers to research best-practices in the humanities and social sciences.

This lesson in part of Software Carpentry workshop and teach novice programmers to write modular code and best practices for using R for data analysis. an introduction to R for non-programmers using gapminder data The goal of this lesson is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. We find that many scientists who come to Software Carpentry workshops use R and want to learn more. The emphasis of these materials is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. Note that this workshop will focus on teaching the fundamentals of the programming language R, and will not teach statistical analysis. The lesson contains more material than can be taught in a day. The instructor notes page has some suggested lesson plans suitable for a one or half day workshop. A variety of third party packages are used throughout this workshop. These are not necessarily the best, nor are they comprehensive, but they are packages we find useful, and have been chosen primarily for their usability.

Una introducción a R utilizando los datos de Gapminder. El objetivo de esta lección es enseñar a las programadoras principiantes a escribir códigos modulares y adoptar buenas prácticas en el uso de R para el análisis de datos. R nos provee un conjunto de paquetes desarrollados por terceros que se usan comúnmente en diversas disciplinas científicas para el análisis estadístico. Encontramos que muchos científicos que asisten a los talleres de Software Carpentry utilizan R y quieren aprender más. Nuestros materiales son relevantes ya que proporcionan a los asistentes una base sólida en los fundamentos de R y enseñan las mejores prácticas del cómputo científico: desglose del análisis en módulos, automatización tareas y encapsulamiento. Ten en cuenta que este taller se enfoca en los fundamentos del lenguaje de programación R y no en el análisis estadístico. A lo largo de este taller se utilizan una variedad de paquetes desarrolados por terceros, los cuales no son necesariamente los mejores ni se encuentran explicadas todas sus funcionalidades, pero son paquetes que consideramos útiles y han sido elegidos principalmente por su facilidad de uso.