Background Sharing research data provides benefit to the general scientific community, but the benefit is less obvious for the investigator who makes his or her data available. Principal Findings We examined the citation history of 85 cancer microarray clinical trial publications with respect to the availability of their data. The 48% of trials with publicly available microarray data received 85% of the aggregate citations. Publicly available data was significantly (p = 0.006) associated with a 69% increase in citations, independently of journal impact factor, date of publication, and author country of origin using linear regression. Significance This correlation between publicly available data and increased literature impact may further motivate investigators to share their detailed research data.

Simmons University and academic health sciences libraries across the USA are partnering to offer a post-master’s certificate program in the area of Inter-Professional Informationist (IPI), for the purpose of bridging the gap between traditional and emergent skills in health sciences librarianship and increasing the diversity in the workforce. A small cohort of librarians in the program will complete seven IPI courses, and partner institutions will connect them with researchers and clinical leaders who will mentor their capstone. This project was made possible in part by the Institute of Museum and Library Services with the Laura Bush 21st Century Librarian Program Grant [RE-17-19-0032-19]. Simmons University, School of Library and Information Science, College of Organizational, Computational and Information Science provides cost-share of the project.

One of the courses included in the IPI program is “Scientific Research Data Management” was taught Fall 2020 by Elaine Martin and Julie Goldman. This course had been an elective in the Simmons School of Library and Information Science curriculum for many years, but underwent a redesign to include and address many of the newer emerging areas related to data services in libraries. For example, the course included “Special Topics” that included Data Curation, Data Skills, Reproducibility, and Informationists. While basic understanding of data management is critical for librarians to work with researchers, there are these emerging areas where librarians can provide even more specialized help to their communities. It is one of the IPI’s project’s goals to bridge the gap between traditional and emergent skills in health sciences librarianship.

This Open Science Framework project site includes curriculum materials for Simmons IPI LIS-532U-OL Scientific Research Data Management (course offered Fall 2020). This course serves as an introduction to the field of scientific data management, and aims to help prepare information professionals and information students for engaging with scientists.

Students will determine whether the amount of air in a balloon changes the distance it will travel on a fishing line. They will collect data from multiple tests and then create a graph to visualize the variation.

Workshop overview for the Data Carpentry Social Sciences curriculum. Data Carpentry’s aim is to teach researchers basic concepts, skills, and tools for working with data so that they can get more done in less time, and with less pain. This workshop teaches data management and analysis for social science research including best practices for data organization in spreadsheets, reproducible data cleaning with OpenRefine, and data analysis and visualization in R. This curriculum is designed to be taught over two full days of instruction. Materials for teaching data analysis and visualization in Python and extraction of information from relational databases using SQL are in development. Interested in teaching these materials? We have an onboarding video and accompanying slides available to prepare Instructors to teach these lessons. After watching this video, please contact team@carpentries.org so that we can record your status as an onboarded Instructor. Instructors who have completed onboarding will be given priority status for teaching at centrally-organized Data Carpentry Social Sciences workshops.

Students roll vehicles down and inclined plane placed at various heights and measure distance traveled. Recorded and graphed data reveals an unexpected data trend (due to friction force).

The goals of this textbook are to help students acquire the technical skills of using software and managing a database, and develop research skills of collecting data, analyzing information and presenting results. We emphasize that the need to investigate the potential and practicality of GIS technologies in a typical planning setting and evaluate its possible applications. GIS may not be necessary (or useful) for every planning application, and we anticipate these readings to provide the necessary foundation for discerning its appropriate use. Therefore, this textbook attempts to facilitate spatial thinking focusing more on open-ended planning questions, which require judgment and exploration, while developing the analytical capacity for understanding a variety of local and regional planning challenges.
While this textbook provides the background for understanding the concepts in GIS as applicable to urban and regional planning, it is best when accompanied by a hands-on tutorial, which will enable readers to develop an in-depth understanding of the specific planning applications of GIS. Chapters in this text book are either composed by the editors using Creative Common materials, or linked to a book chapter scanned copy in the library reserve. In the end of each chapter, we also provided several discussion questions, together with contextual applications through some web links.

The wide array of datasets provided in this collection affords educators and learners alike an understanding of several large networks from state roads to the internet. Access data on social networks, Wikipedia use and e-mail communication and much more.

Working as if they are engineers aiming to analyze and then improve data collection devices for precision agriculture, students determine how accurate temperature sensors are by comparing them to each other. Teams record soil temperature data during a class period while making changes to the samples to mimic real-world crop conditions—such as the addition of water and heat and the removal of the heat. Groups analyze their collected data by finding the mean, median, mode, and standard deviation. Then, the class combines all the team data points in order to compare data collected from numerous devices and analyze the accuracy of their recording devices by finding the standard deviation of temperature readings at each minute. By averaging the standard deviations of each minute’s temperature reading, students determine the accuracy of their temperature sensors. Students present their findings and conclusions, including making recommendations for temperature sensor improvements.

As our society increasingly calls for evidence-based decision making, it is important to consider how and when we can draw valid inferences from data. This module will use four recent research studies to highlight key elements of a statistical investigation.

Statistical thinking is a way of understanding a complex world by describing it in relatively simple terms that nonetheless capture essential aspects of its structure, and that also provide us some idea of how uncertain we are about our knowledge. The foundations of statistical thinking come primarily from mathematics and statistics, but also from computer science, psychology, and other fields of study.

The purpose of this course is to provide background in the ways in which psychologists evaluate data collected from research projects. A researcher may gather many pieces of data that describe a group of research subjects and there are common ways in which these pieces of information are presented. Secondly, statistical tests can help investigators draw inferences about the relationship of the research sample to the general population it is supposed to represent. As a student of psychology or any other discipline that uses research data to explore ideas, it is important that you know how data is evaluated and that you gain an understanding of the ways in which these procedures help to summarize and clarify data.

This site teaches High Schoolers Modeling with Geometry through a series of 1548 questions and interactive activities aligned to 12 Common Core mathematics skills.

A whirl-wind tour of the statistics used in behavioral science research, covering topics including: data visualization, building your own null-hypothesis distribution through permutation, useful parametric distributions, the generalized linear model, and model-based analyses more generally. Familiarity with MATLAB®, Octave, or R will be useful, prior experience with statistics will be helpful but is not essential. This course is intended to be a ground-up sketch of a coherent, alternative perspective to the “null-hypothesis significance testing” method for behavioral research (but don’t worry if you don’t know what this means).

This webinar will introduce the integration of JASP Statistical Software (https://jasp-stats.org/) with the Open Science Framework (OSF; https://osf.io). 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, Mendeley, and now is integrated with JASP, to streamline workflows and increase efficiency.

Students will use their knowledge of stem and leaf plots to represent temperature in various locations outdoors.

STUDENT ACTIVITY - 4th --TXThis is a distance-learning lesson students can complete at home.The student will use your their knowledge of stem and leaf plots to represent temperature in various locations outdoors.This activity was created by Out Teach (out-teach.org), a nonprofit providing outdoor experiential learning to transform Science education for students in under-served communities.

Students learn to identify and evaluate the authority of data and the authors who publish interpretations of data. They review a dataset from FRED® and determine the authority of the dataset based on shared criteria. They also review that same dataset in several interpretations published in blogs and articles, applying the criteria to analyze the authority. Students discuss how they determined authority in pairs and share back their thoughts to the class.

Replication is the cornerstone of a cumulative science. However, new tools and technologies, massive amounts of data, interdisciplinary approaches, and the complexity of the questions being asked are complicating replication efforts, as are increased pressures on scientists to advance their research. As full replication of studies on independently collected data is often not feasible, there has recently been a call for reproducible research as an attainable minimum standard for assessing the value of scientific claims. This requires that papers in experimental science describe the results and provide a sufficiently clear protocol to allow successful repetition and extension of analyses based on original data. The importance of replication and reproducibility has recently been exemplified through studies showing that scientific papers commonly leave out experimental details essential for reproduction, studies showing difficulties with replicating published experimental results, an increase in retracted papers, and through a high number of failing clinical trials. This has led to discussions on how individual researchers, institutions, funding bodies, and journals can establish routines that increase transparency and reproducibility. In order to foster such aspects, it has been suggested that the scientific community needs to develop a “culture of reproducibility” for computational science, and to require it for published claims. We want to emphasize that reproducibility is not only a moral responsibility with respect to the scientific field, but that a lack of reproducibility can also be a burden for you as an individual researcher. As an example, a good practice of reproducibility is necessary in order to allow previously developed methodology to be effectively applied on new data, or to allow reuse of code and results for new projects. In other words, good habits of reproducibility may actually turn out to be a time-saver in the longer run. We further note that reproducibility is just as much about the habits that ensure reproducible research as the technologies that can make these processes efficient and realistic. Each of the following ten rules captures a specific aspect of reproducibility, and discusses what is needed in terms of information handling and tracking of procedures. If you are taking a bare-bones approach to bioinformatics analysis, i.e., running various custom scripts from the command line, you will probably need to handle each rule explicitly. If you are instead performing your analyses through an integrated framework (such as GenePattern, Galaxy, LONI pipeline, or Taverna), the system may already provide full or partial support for most of the rules. What is needed on your part is then merely the knowledge of how to exploit these existing possibilities.

This article offers a short guide to the steps scientists can take to ensure that their data and associated analyses continue to be of value and to be recognized. In just the past few years, hundreds of scholarly papers and reports have been written on questions of data sharing, data provenance, research reproducibility, licensing, attribution, privacy, and more—but our goal here is not to review that literature. Instead, we present a short guide intended for researchers who want to know why it is important to “care for and feed” data, with some practical advice on how to do that. The final section at the close of this work (Links to Useful Resources) offers links to the types of services referred to throughout the text.

The Third Grade Elementary Framework for Science and Integrated Subjects, Weather, uses the phenomena of extreme weather events.  It is part of Elementary Framework for Science and Integrated Subjects project, a statewide Clime Time collaboration among ESD 123, ESD 105, North Central ESD, and the Office of Superintendent of Public Instruction. Development of the resources is in response to a need for research- based science lessons for elementary teachers that are integrated with English language arts, mathematics and other subjects such as social studies. The template for Elementary Science and Integrated Subjects  can serve as an organized, coherent and research-based roadmap for teachers in the development of their own NGSS aligned science lessons.  Lessons can also be useful for classrooms that have no adopted curriculum as well as to serve as enhancements for  current science curriculum. The EFSIS project brings together grade level teams of teachers to develop lessons or suites of lessons that are 1) pnenomena based, focused on grade level Performance Expectations, and 2) leverage ELA and Mathematics Washington State Learning Standards.