This webinar outlines how to use the free Open Science Framework (OSF) as an Electronic Lab Notebook for personal work or private collaborations. Fundamental features we cover include how to record daily activity, how to store images or arbitrary data files, how to invite collaborators, how to view old versions of files, and how to connect all this usage to more complex structures that support the full work of a lab across multiple projects and experiments.

Using thermometers, cotton balls, string and water, students make simple psychrometers—a tool that measures humidity. They learn the difference between relative humidity (the ratio of water vapor content to water vapor carrying capacity) and dew point (the temperature at which dew forms). Teams collect data using their homemade psychrometers and then calculate relative humidity inside and outside, comparing their results to an off-the-shelf psychrometer (if available). A lab worksheet is provided for data collection and calculation. As a real-world connection, students learn that humidity and air density is taken into consideration by engineers for many design projects. To conclude, they answer and discuss analysis and application questions.

Students will analyze census data and graphs that demonstrate how certain aspects of the lives of African-Americans have changed since civil rights leader Martin Luther King Jr. delivered his “I Have a Dream” speech in 1963. Students will select a fact from these data, facts from other sources, and a historical photograph to include on a poster about King.

The Image Data Resource (IDR) is a public repository of reference image datasets from published scientific studies. IDR enables access, search and analysis of these highly annotated datasets. Datasets are usually CC0 or CC BY 4.0.

This lesson shows how to use Python and skimage to do basic image processing. With support from an NSF iUSE grant, Dr. Tessa Durham Brooks and Dr. Mark Meysenburg at Doane College, Nebraska, USA have developed a curriculum for teaching image processing in Python. This lesson is currently being piloted at different institutions. This pilot phase will be followed by a clean-up phase to incorporate suggestions and feedback from the pilots into the lessons and to make the lessons teachable by the broader community. Development for these lessons has been supported by a grant from the Sloan Foundation.

Students will examine data on the number of immigrants in the United States, to create bar graphs and line graphs with appropriate scales. Students will then compare and analyze their graphs to draw conclusions about the data.

Microsoft Excel is extremely useful for many different types of digital scholarship projects. This one looks at the ability of Excel to create time lines for historical projects using an Excel template developed for project time lines. Before starting I will warn the reader that because of the way Excel stores and handles dates, these time lines only work for dates after Jan. 1, 1900. There are some potential fixes for this that I hope to address in the future.

Students will examine and interact with two economic data websites—FRED®, of the Federal Reserve Bank of St. Louis, and the Bureau of Economic Analysis (BEA)—to develop their understanding of the information creation process. They will learn about differences between data aggregators and data sources and the capabilities and constraints of each in presenting economic data. Students will examine in detail gross domestic product (GDP) data.

The last ten years have witnessed increasing awareness of questionable research practices (QRPs) in the life sciences, including p-hacking, HARKing, lack of replication, publication bias, low statistical power and lack of data sharing (see Figure 1). Concerns about such behaviours have been raised repeatedly for over half a century but the incentive structure of academia has not changed to address them. Despite the complex motivations that drive academia, many QRPs stem from the simple fact that the incentives which offer success to individual scientists conflict with what is best for science. On the one hand are a set of gold standards that centuries of the scientific method have proven to be crucial for discovery: rigour, reproducibility, and transparency. On the other hand are a set of opposing principles born out of the academic career model: the drive to produce novel and striking results, the importance of confirming prior expectations, and the need to protect research interests from competitors. Within a culture that pressures scientists to produce rather than discover, the outcome is a biased and impoverished science in which most published results are either unconfirmed genuine discoveries or unchallenged fallacies. This observation implies no moral judgement of scientists, who are as much victims of this system as they are perpetrators.

This article discusses how the study of weather can meet the NCTM Data Analysis and Probability standard. Links to lessons for grades K-2 and 3-5 are provided.

This Module, the second in a series on intensive intervention, offers information on making data-based instructional decisions. Specifically, the resource discusses collecting and analyzing progress monitoring and diagnostic assessment data. Developed in collaboration with the National Center on Intensive Intervention at American Institutes for Research and the CEEDAR Center, this resource is designed for individuals who will be implementing intensive interventions (e.g., special education teachers, reading specialists, interventionists) (est. completion time: 3 hours).

Data Carpentry lesson to learn how to work with Amazon AWS cloud computing and how to transfer data between your local computer and cloud resources. The cloud is a fancy name for the huge network of computers that host your favorite websites, stream movies, and shop online, but you can also harness all of that computing power for running analyses that would take days, weeks or even years on your local computer. In this lesson, you’ll learn about renting cloud services that fit your analytic needs, and how to interact with one of those services (AWS) via the command line.

As rapidly changing technology enables researchers to collect large, complex datasets with relative ease, the need to effectively manage these data increases in kind. This is the first lesson in a series of education modules intended to provide a broad overview of various topics related to research data management. It covers: trends in data collection, storage and loss, the importance and benefits of data management, and an introduction to the data life cycle.

Data Carpentry lesson to understand data structures and common storage and transfer formats for spatial data. The goal of this lesson is to provide an introduction to core geospatial data concepts. It is intended for learners who have no prior experience working with geospatial data, and as a pre-requisite for the R for Raster and Vector Data lesson . This lesson can be taught in approximately 75 minutes and covers the following topics: Introduction to raster and vector data format and attributes Examples of data types commonly stored in raster vs vector format Introduction to categorical vs continuous raster data and multi-layer rasters Introduction to the file types and R packages used in the remainder of this workshop Introduction to coordinate reference systems and the PROJ4 format Overview of commonly used programs and applications for working with geospatial data The Introduction to R for Geospatial Data lesson provides an introduction to the R programming language while the R for Raster and Vector Data lesson provides a more in-depth introduction to visualization (focusing on geospatial data), and working with data structures unique to geospatial data. The R for Raster and Vector Data lesson assumes that learners are already familiar with both geospatial data concepts and the core concepts of the R language.

Data Carpentry lesson to open, work with, and plot vector and raster-format spatial data in R. The episodes in this lesson cover how to open, work with, and plot vector and raster-format spatial data in R. Additional topics include working with spatial metadata (extent and coordinate reference systems), reprojecting spatial data, and working with raster time series data.

The general minimum prerequisite for understanding this book is the intellectual matur­ity of a junior-level (third-year) college student in an accredited four-year engineering curriculum. A mathematical second-order system is represented in this book primarily by a single second-order ODE, not in the state-space form by a pair of coupled first-order ODEs. Similarly, a two-degrees-of-freedom (fourth-order) system is represented by two coupled second-order ODEs, not in the state-space form by four coupled first-order ODEs. The book does not use bond graph modeling, the general and powerful, but complicated, modern tool for analysis of complex, multidisciplinary dynamic systems. The homework problems at the ends of chapters are very important to the learning objectives, so the author attempted to compose problems of practical interest and to make the problem statements as clear, correct, and unambiguous as possible. A major focus of the book is computer calculation of system characteristics and responses and graphical display of results, with use of basic (not advanced) MATLAB commands and programs. The book includes many examples and homework problems relevant to aerospace engineering, among which are rolling dynamics of flight vehicles, spacecraft actuators, aerospace motion sensors, and aeroelasticity. There are also several examples and homework problems illustrating and validating theory by using measured data to identify first- and second-order system dynamic characteristics based on mathematical models (e.g., time constants and natural frequencies), and system basic properties (e.g., mass, stiffness, and damping). Applications of real and simulated experimental data appear in many homework problems. The book contains somewhat more material than can be covered during a single standard college semester, so an instructor who wishes to use this as a one-semester course textbook should not attempt to cover the entire book, but instead should cover only those parts that are most relevant to the course objectives.

This is a recording of a 45 minute introductory webinar on preprints. With our guest speaker Philip Cohen, we’ll cover what preprints/postprints are, the benefits of preprints, and address some common concerns researcher may have. We’ll show how to determine whether you can post preprints/postprints, and also demonstrate how to use OSF preprints (https://osf.io/preprints/) to share preprints. The OSF is the flagship product of the Center for Open Science, a non-profit technology start-up dedicated to improving the alignment between scientific values and scientific practices. Learn more at cos.io and osf.io, or email contact@cos.io.

In this webinar, Doctors David Mellor (Center for Open Science) and Stavroula Kousta (Nature Human Behavior) discuss the Registered Reports publishing workflow and the benefits it may bring to funders of research. Dr. Mellor details the workflow and what it is intended to do, and Dr. Kousta discusses the lessons learned at Nature Human Behavior from their efforts to implement Registered Reports as a journal.

The goal of this lesson is to provide an introduction to R for learners working with geospatial data. It is intended as a pre-requisite for the R for Raster and Vector Data lesson for learners who have no prior experience using R. This lesson can be taught in approximately 4 hours and covers the following topics: Working with R in the RStudio GUI Project management and file organization Importing data into R Introduction to R’s core data types and data structures Manipulation of data frames (tabular data) in R Introduction to visualization Writing data to a file The the R for Raster and Vector Data lesson provides a more in-depth introduction to visualization (focusing on geospatial data), and working with data structures unique to geospatial data.