Expectations by funders for transparent and reproducible methods are on the rise. This session covers expectations for preregistration, data sharing, and open access results of three key funders of education research including the Institute of Education Sciences, the National Science Foundation, and Arnold Ventures. Presenters cover practical resources for meeting these requirements such as the Registry for Efficacy and Effectiveness Studies (REES), the Open Science Framework (OSF), and EdArXiv. Presenters: Jessaca Spybrook, Western Michigan University Bryan Cook, University of Virginia David Mellor, Center for Open Science

What is metadata? Metadata is data (or documentation) that describes and provides context for data and it is everywhere around us. Metadata allows us to understand the details of a dataset, including: where it was collected, how it was collected, what gaps in the data mean, what the units of measurement are, who collected the data, how it should be attributed etc. By creating and providing good descriptive metadata for our own data, we enable others to efficiently discover and use the data products from our research. This lesson explores the importance of metadata to data authors, users of the data and organizations, and highlights the utility of metadata. It provides an overview of the different metadata standards that exist, and the core elements that are consistent across them. It guides users in selecting a metadata standard to work with and introduces the best practices needed for writing a high quality metadata record.

Students learn about life-cycle assessment and how engineers use this technique to determine the environmental impact of everyday products and processes. As they examine what’s involved in making and consuming cupcakes, a snack enjoyed by millions of people every year, students learn about the production, use and disposal phases of an object’s life cycle. With the class organized into six teams, students calculate data for each phase of a cupcake’s life cycle—wet ingredients, dry ingredients, baking materials, oven baking, frosting, liner disposal—and calculate energy usage and greenhouse gases emitted from making one cupcake. They use ratios and fractions, and compare options for some of the life-cycle stages, such as different paper wrapper endings (disposal to landfills or composting) in order to make a life-cycle plan with a lower environmental impact. This activity opens students’ eyes to see the energy use in the cradle-to-grave lives of everyday products. Pre/post-quizzes, worksheets, activity cards, Excel® workbook and visual aids are provided.

The Mozilla Science Lab is developing an Open Data Training Program. This repository will be where we build and share our curriculum and resources for open data.

This resource was created by Prairie Compton, in collaboration with Dawn DeTurk, Hannah Blomstedt, and Julie Albrecht, as part of ESU2's Integrating the Arts project. This project is a four year initiative focused on integrating arts into the core curriculum through teacher education, practice, and coaching.

(Nota: Esta es una traducción de un recurso educativo abierto creado por el Departamento de Educación del Estado de Nueva York (NYSED) como parte del proyecto "EngageNY" en 2013. Aunque el recurso real fue traducido por personas, la siguiente descripción se tradujo del inglés original usando Google Translate para ayudar a los usuarios potenciales a decidir si se adapta a sus necesidades y puede contener errores gramaticales o lingüísticos. La descripción original en inglés también se proporciona a continuación.)

Este módulo de 10 días se basa en conceptos de grado 2 sobre datos, gráficos y parcelas de línea. Los dos temas en este módulo se centran en generar y analizar datos categóricos y de medición. Al final del módulo, los estudiantes están trabajando con una mezcla de gráficos de imágenes escalados, gráficos de barras y gráficos de línea para resolver problemas utilizando datos categóricos y de medición.

Encuentre el resto de los recursos matemáticos de Engageny en https://archive.org/details/engageny-mathematics.

English Description:
This 10-day module builds on Grade 2 concepts about data, graphing, and line plots. The two topics in this module focus on generating and analyzing categorical and measurement data.  By the end of the module, students are working with a mixture of scaled picture graphs, bar graphs, and line plots to problem solve using both categorical and measurement data.

Find the rest of the EngageNY Mathematics resources at https://archive.org/details/engageny-mathematics.

Nebraska standards that detail the essential knowledge and skills necessary for educators to effectively use data to inform instructional and programmatic decisions.

This record includes training materials associated with 'Network Know-how and Data Handling' workshops offered by Australia's Academic Research Network (AARNet). The workshops are delivered to library and eResearch staff at universities, as well as researcher communities, as train-the-trainer events, part of a broader infrastructure literacy strategy.

This workshop is a ‘train-the-trainer’ session that covers topics such as jargon busting, network literacy and data movement solutions. The workshop will also provide a peek at some collaborative research tools such as Jupyter Notebooks and CloudStor. You will learn about networks, integrated tools, data and storage and where all these things fit in the researcher’s toolkit.

This workshop is targeted at staff who would like to be more confident in giving advice to researchers about the options available to them. It is especially tailored for those with little to no technical knowledge and includes a hands-on component, using basic programming commands, but requires no previous knowledge of programming.

The New England Collaborative Data Management Curriculum (NECDMC) project is led by the Lamar Soutter Library at the University of Massachusetts Medical School in partnership with several libraries in the New England region.

NECDMC is an instructional tool for teaching data management best practices to undergraduates, graduate students, and researchers in the health sciences, sciences, and engineering disciplines. Each of the curriculum’s seven online instructional modules aligns with the National Science Foundation’s data management plan recommendations and addresses universal data management challenges. Included in the curriculum is a collection of actual research cases that provides a discipline specific context to the content of the instructional modules. These cases come from a range of research settings such as clinical research, biomedical labs, an engineering project, and a qualitative behavioral health study. Additional research cases will be added to the collection on an ongoing basis. Each of the modules can be taught as a stand-alone class or as part of a series of classes. Instructors are welcome to customize the content of the instructional modules to meet the learning needs of their students and the policies and resources at their institutions

Through the Public Library Partnerships Project (PLPP), DPLA has been working with existing DPLA Service Hubs to provide digital skills training for public librarians and connect them sustainably with state and regional resources for digitizing, describing, and exhibiting their cultural heritage content.

During the project, DPLA collaborated with trainers at Digital Commonwealth, Digital Library of Georgia, Minnesota Digital Library, Montana Memory Project, and Mountain West Digital Library to write and iterate a workshop curriculum based on documented best practices. Through the project workshops, we used this curriculum to introduce 150 public librarians to the digitization process.

Now at the end of the project, we’ve made this curriculum available in a self-guided version intended for digitization beginners from a variety of cultural heritage institutions. Each module includes a video presentation, slides with notes in Powerpoint, and slides in PDF. Please feel free to share, reuse, and adapt these materials.

This webinar walks you through the basics of creating an OSF project, structuring it to fit your research needs, adding collaborators, and tying your favorite online tools into your project structure. OSF is a free, open source web application built by the Center for Open Science, a non-profit dedicated to improving the alignment between scientific values and scientific practices. OSF is part collaboration tool, part version control software, and part data archive. It is designed to connect to popular tools researchers already use, like Dropbox, Box, Github, and Mendeley, to streamline workflows and increase efficiency.

This video will introduce users of the Open Science Framework into some of the more advanced features.

Files for this webinar are available at: https://osf.io/ewhvq/ This webinar focuses on how to use the Open Science Framework (OSF) to tie together and organize multiple projects. We look at example structures appropriate for organizing classroom projects, a line of research, or a whole lab's activity. We discuss the OSF's capabilities for using projects as templates, linking projects, and forking projects as well as some considerations for using each of those capabilities when designing a structure for your own project. The OSF is a free, open source web application built to help researchers manage their workflows. The OSF is part collaboration tool, part version control software, and part data archive. The OSF connects to popular tools researchers already use, like Dropbox, Box, Github and Mendeley, to streamline workflows and increase efficiency.

This webinar will introduce how to use the Open Science Framework (OSF; https://osf.io) in a Classroom. The OSF is a free, open source web application built to help researchers manage their workflows. The OSF is part collaboration tool, part version control software, and part data archive. The OSF connects to popular tools researchers already use, like Dropbox, Box, Github and Mendeley, to streamline workflows and increase efficiency. This webinar will discuss how to introduce reproducible research practices to students, show ways of tracking student activity, and introduce the use of Templates and Forks on the OSF to allow students to easily make new class projects. 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.

Scientific reproducibility has been at the forefront of many news stories and there exist numerous initiatives to help address this problem. We posit that a contributor is simply a lack of specificity that is required to enable adequate research reproducibility. In particular, the inability to uniquely identify research resources, such as antibodies and model organisms, makes it difficult or impossible to reproduce experiments even where the science is otherwise sound. In order to better understand the magnitude of this problem, we designed an experiment to ascertain the “identifiability” of research resources in the biomedical literature. We evaluated recent journal articles in the fields of Neuroscience, Developmental Biology, Immunology, Cell and Molecular Biology and General Biology, selected randomly based on a diversity of impact factors for the journals, publishers, and experimental method reporting guidelines. We attempted to uniquely identify model organisms (mouse, rat, zebrafish, worm, fly and yeast), antibodies, knockdown reagents (morpholinos or RNAi), constructs, and cell lines. Specific criteria were developed to determine if a resource was uniquely identifiable, and included examining relevant repositories (such as model organism databases, and the Antibody Registry), as well as vendor sites. The results of this experiment show that 54% of resources are not uniquely identifiable in publications, regardless of domain, journal impact factor, or reporting requirements. For example, in many cases the organism strain in which the experiment was performed or antibody that was used could not be identified. Our results show that identifiability is a serious problem for reproducibility. Based on these results, we provide recommendations to authors, reviewers, journal editors, vendors, and publishers. Scientific efficiency and reproducibility depend upon a research-wide improvement of this substantial problem in science today.

OpenAlex is a fully open catalog of the global research system. Its dataset describes scholarly entities and how those entities are connected to each other. OpenAlex provides documentation and guidance on how to use API to retrieve thier data. Thus, one can this resource to prepare an API workshop or for professional development.

Lesson on OpenRefine for social scientists. A part of the data workflow is preparing the data for analysis. Some of this involves data cleaning, where errors in the data are identifed and corrected or formatting made consistent. This step must be taken with the same care and attention to reproducibility as the analysis. OpenRefine (formerly Google Refine) is a powerful free and open source tool for working with messy data: cleaning it and transforming it from one format into another. This lesson will teach you to use OpenRefine to effectively clean and format data and automatically track any changes that you make. Many people comment that this tool saves them literally months of work trying to make these edits by hand.

The Open Research Toolkit was created by Christopher Eaker during Faculty Development Leave, Fall 2021. While this toolkit was designed for librarians for learning open research concepts and skills and teaching them at their institutions, it would be useful for anyone interested in learning more about open research. Any questions related to this content can be directed to the author.

The ORT YouTube Channel is found here: http://doi.org/10.7290/ORT_Videos

The Open Research Toolkit is an Open Educational Resource, and is available under Creative Commons Attribution 4.0 International (CC BY 4.0). You may re-use and copy information from this toolkit with attribution. In addition, some of the materials referenced in this toolkit (e.g. some materials linked to and created by others) might be copyright protected; that will be indicated as best as possible, but no guarantees are made as to accuracy of that information. The user should check restrictions of any material prior to reusing it.

About This Document: This manual was assembled and is being updated by Professor Benjamin Le (@benjaminle), who is on the faculty in the Department of Psychology at Haverford College. The primary goal of this text is to provide guidance to his senior thesis students on how to conduct research in his lab by working within general principles that promote research transparency using the specific open science practices described here. While it is aimed at undergraduate psychology students, hopefully it will be of use to other faculty/researchers/students who are interested in adopting open science practices in their labs.