Data Curation Primer: SPSS
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Module 8 Wiki Guide
Best Practices for Biomedical Research Data Management Harvard Medical School, The Francis A. Countway Library of Medicine Module 8 Wiki Guide Learning Objectives and Outcomes: 1. Emphasize characteristics of long-term data curation and preservation that build on and extend active data management ● It is the purview of permanent archiving and preservation to take over stewardship and ensure that the data do not become technologically obsolete and no longer permanently accessible. ○ The selection of a repository to ensure that certain technical processes are performed routinely and reliably to maintain data integrity ○ Determining the costs and steps necessary to address preservation issues such as technological obsolescence inhibiting data access ○ Consistent, citable access to data and associated contextual records ○ Ensuring that protected data stays protected through repository-governed access control ● Data Management ○ Refers to the handling, manipulation, and retention of data generated within the context of the scientific process ○ Use of this term has become more common as funding agencies require researchers to develop and implement structured plans as part of grant-funded project activities ● Digital Stewardship ○ Contributions to the longevity and usefulness of digital content by its caretakers that may occur within, but often outside of, a formal digital preservation program ○ Encompasses all activities related to the care and management of digital objects over time, and addresses all phases of the digital object lifecycle 2. Distinguish between preservation and curation ● Digital Curation ○ The combination of data curation and digital preservation ○ There tends to be a relatively strong orientation toward authenticity, trustworthiness, and long-term preservation ○ Maintaining and adding value to a trusted body of digital information for future and current use. -
Fortran Reference Guide
FORTRAN REFERENCE GUIDE Version 2018 TABLE OF CONTENTS Preface............................................................................................................ xv Audience Description......................................................................................... xv Compatibility and Conformance to Standards............................................................ xv Organization................................................................................................... xvi Hardware and Software Constraints...................................................................... xvii Conventions................................................................................................... xvii Related Publications........................................................................................ xviii Chapter 1. Language Overview............................................................................... 1 1.1. Elements of a Fortran Program Unit.................................................................. 1 1.1.1. Fortran Statements................................................................................. 1 1.1.2. Free and Fixed Source............................................................................. 2 1.1.3. Statement Ordering................................................................................. 2 1.2. The Fortran Character Set.............................................................................. 3 1.3. Free Form Formatting.................................................................................. -
SPSS to Orthosim File Conversion Utility Helpfile V.1.4
SPSS to Orthosim File Conversion Utility Helpfile v.1.4 Paul Barrett Advanced Projects R&D Ltd. Auckland New Zealand email: [email protected] Web: www.pbarrett.net 30th December, 2019 Contents 3 Table of Contents Part I Introduction 5 1 Installation Details ................................................................................................................................... 7 2 Extracting Matrices from SPSS - Cut and Paste ................................................................................................................................... 8 3 Extracting Matrices from SPSS: Orthogonal Factors - E.x..c..e..l. .E..x..p..o..r.t................................................................................................................. 17 4 Extracting Matrices from SPSS: Oblique Factors - Exce.l. .E..x..p..o..r..t...................................................................................................................... 24 5 Creating Orthogonal Factor Orthosim Files ................................................................................................................................... 32 6 Creating Oblique Factor Orthosim Files ................................................................................................................................... 41 3 Paul Barrett Part I 6 SPSS to Orthosim File Conversion Utility Helpfile v.1.4 1 Introduction SPSS-to-Orthosim converts SPSS 11/12/13/14 factor loading and factor correlation matrices into the fixed-format .vf (simple ASCII text) files -
Introduction to Linux on System Z
IBM Linux and Technology Center Introduction to Linux on System z Mario Held IBM Lab Boeblingen, Germany © 2009 IBM Corporation IBM Linux and Technology Center Trademarks The following are trademarks of the International Business Machines Corporation in the United States, other countries, or both. Not all common law marks used by IBM are listed on this page. Failure of a mark to appear does not mean that IBM does not use the mark nor does it mean that the product is not actively marketed or is not significant within its relevant market. Those trademarks followed by ® are registered trademarks of IBM in the United States; all others are trademarks or common law marks of IBM in the United States. For a complete list of IBM Trademarks, see www.ibm.com/legal/copytrade.shtml: *, AS/400®, e business(logo)®, DBE, ESCO, eServer, FICON, IBM®, IBM (logo)®, iSeries®, MVS, OS/390®, pSeries®, RS/6000®, S/30, VM/ESA®, VSE/ESA, WebSphere®, xSeries®, z/OS®, zSeries®, z/VM®, System i, System i5, System p, System p5, System x, System z, System z9®, BladeCenter® The following are trademarks or registered trademarks of other companies. Adobe, the Adobe logo, PostScript, and the PostScript logo are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States, and/or other countries. Cell Broadband Engine is a trademark of Sony Computer Entertainment, Inc. in the United States, other countries, or both and is used under license therefrom. Java and all Java-based trademarks are trademarks of Sun Microsystems, Inc. in the United States, other countries, or both. -
Integrating Excel, SQL, and SPSS Within an Introductory Finance Intrinsic Value Assignment
Journal of Finance and Accountancy Volume 20, September, 2015 Integrating Excel, SQL, and SPSS within an Introductory Finance Intrinsic Value Assignment Richard Walstra Dominican University Anne Drougas Dominican University Steve Harrington Dominican University ABSTRACT In 2013, the Association to Advance Collegiate Schools of Business (AACSB) issued revised accreditation standards to address a “new era” of business education. The standards recognize that students will be entering a data-driven world and need skills in information technology to analyze and manage data. Employer surveys also emphasize the importance of technological skills and knowledge in a rapidly changing environment. To address these challenges, business faculty in all disciplines must adapt course content to incorporate software tools and statistical applications and integrate active learning tools that provide students with hands-on experience. This paper describes a technology-based, business valuation assignment instructors can employ within an undergraduate managerial finance course. The assignment draws upon historical financial data from the restaurant industry as students predict the intrinsic value of a firm through the application of various software products. Students receive data in an Access database and create SQL queries to determine free cash flows before transferring information into Excel for further analysis. Students continue by uploading key data elements into SPSS and performing statistical analysis on three growth models in order to identify a desired predictor of intrinsic value. The assignment develops students’ abilities to navigate software tools, understand statistical concepts, and apply quantitative decision making. Key Words: Business valuation, growth rates, software tools Copyright statement: Authors retain the copyright to the manuscripts published in AABRI journals. -
AIX Migration to Cloud with IBM Power Virtual Server
AIX Migration to Cloud with IBM Power Virtual Server An IBM Systems Lab Services Tutorial Aaron Bolding Berjis Patel Vess Natchev [email protected] TABLE OF CONTENTS CHAPTER 1: SOLUTION OVERVIEW............................. 1 Introduction ................................................................................ 1 Use Cases .................................................................................. 1 Migration via PowerVC OVA ..................................................... 1 Transfer System Backup Using the Public Internet ..................... 2 Transfer System Backup Using Cloud Object Storage ................. 2 Solution Components and Requirements ........................................ 2 Components .......................................................................... 2 Migration via PowerVC OVA ..................................................... 2 Transfer System Backup Using the Public Internet ..................... 2 Transfer System Backup Using Cloud Object Storage ................. 2 Requirements ........................................................................ 3 Solution Diagrams ....................................................................... 3 Transfer System Backup Using the Public Internet ..................... 3 Transfer System Backup Using Cloud Object Storage ................. 4 CHAPTER 2: IMPLEMENTATION .................................. 5 Migration via PowerVC OVA .......................................................... 5 Procedure to Configure IBM Cloud Object Storage ..................... -
Undergraduate Biocuration: Developing Tomorrow’S Researchers While Mining Today’S Data
The Journal of Undergraduate Neuroscience Education (JUNE), Fall 2015, 14(1):A56-A65 ARTICLE Undergraduate Biocuration: Developing Tomorrow’s Researchers While Mining Today’s Data Cassie S. Mitchell, Ashlyn Cates, Renaid B. Kim, & Sabrina K. Hollinger Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA 30332. Biocuration is a time-intensive process that involves managerial positions. We detail the undergraduate extraction, transcription, and organization of biological or application and training processes and give detailed job clinical data from disjointed data sets into a user-friendly descriptions for each position on the assembly line. We database. Curated data is subsequently used primarily for present case studies of neuropathology curation performed text mining or informatics analysis (bioinformatics, entirely by undergraduates, namely the construction of neuroinformatics, health informatics, etc.) and secondarily experimental databases of Amyotrophic Lateral Sclerosis as a researcher resource. Biocuration is traditionally (ALS) transgenic mouse models and clinical data from ALS considered a Ph.D. level task, but a massive shortage of patient records. Our results reveal undergraduate curators to consolidate the ever-mounting biomedical “big biocuration is scalable for a group of 8-50+ with relatively data” opens the possibility of utilizing biocuration as a minimal required resources. Moreover, with average means to mine today’s data while teaching students skill accuracy rates greater than 98.8%, undergraduate sets they can utilize in any career. By developing a biocurators are equivalently accurate to their professional biocuration assembly line of simplified and counterparts. Initial training to be completely proficient at compartmentalized tasks, we have enabled biocuration to the entry-level takes about five weeks with a minimal be effectively performed by a hierarchy of undergraduate student time commitment of four hours/week. -
A Brief Introduction to the Data Curation Profiles
Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success A Brief Introduction to the Data Curation Profiles Jake Carlson Data Services Specialist Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success Setting the stage Since 2004: Purdue Interdisciplinary Research Initiative revealed many data needs on campus What faculty said… • Not sure how or whether to share data • Lack of time to organize data sets • Need help describing data for discovery • Want to find new ways to manage data • Need help archiving data sets/collections Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success “Investigating Data Curation Profiles across Research Domains” This lead to a research award from IMLS • Goals of the project: – A better understanding of the practices, attitudes and needs of researchers in managing and sharing their data. – Identify possible roles for librarians and the skill sets they will need to facilitate data sharing and curation. – Develop “data curation profiles” – a tool for librarians and others to gather information on researcher needs for their data and to inform curation services. – Purdue: Brandt—PI, Carlson—Proj Mgr, Witt—coPI; GSLIS UIUC: Palmer—co-PI, Cragin—Proj Mgr Data Curation Profiles http://www.datacurationprofiles.org Access. Knowledge. Success Data Curation Profiles Methodology • Initial interview— based on “Conducting a Data Interview” poster, then pre-profile based on model of data in a few published papers • Interviews were transcribed, reviewed • Coding, wanted more detail -> 2nd Interview • Developed and revised a template for Profile “In our experience, one of the most effective tactics for eliciting datasets for the collection is a simple librarian- researcher interview. -
IBM DB2 for Z/OS: the Database for Gaining a Competitive Advantage!
Why You Should Read This Book Tom Ramey, Director, DB2 for z/OS IBM Silicon Valley Laboratory “This book is a ‘must read’ for Enterprise customers and contains a wealth of valuable information! “It is clear that there is a technology paradigm shift underway, and this is opening enormous opportunities for companies in all industries. Adoption of Cloud, Mobile, and Analytics promises to revolutionize the way we do business and will add value to a company’s business processes across all functions from sales, marketing, procurement, manufacturing and finance. “IT will play a significant role enabling this shift. Read this book and find out how to integrate the heart of your infrastructure, DB2 for z/OS, with new technologies in order to maximize your investment and drive new value for your customers.” Located at IBM’s Silicon Valley Laboratory, Tom is the director of IBM’s premiere relational database management system. His responsibilities include Architecture, Development, Service, and Customer Support for DB2. He leads development labs in the United States, Germany, and China. Tom works closely with IBM’s largest clients to ensure that DB2 for z/OS continues as the leading solution for modern applications, encompassing OLTP to mobile to analytics. At the same time he continues an uncompromising focus on meeting the needs of the most demanding operational environments on Earth, through DB2’s industry- leading performance, availability, scaling, and security capabilities. IBM DB2 for z/OS: The Database for Gaining a Competitive Advantage! Shantan Kethireddy Jane Man Surekha Parekh Pallavi Priyadarshini Maryela Weihrauch MC Press Online, LLC Boise, ID 83703 USA IBM DB2 for z/OS: The Database for Gaining a Competitive Advantage! Shantan Kethireddy, Jane Man, Surekha Parekh, Pallavi Priyadarshini, and Maryela Weihrauch First Edition First Printing—October 2015 © Copyright 2015 IBM. -
RACF Command Tips
RACF Command Tips SHARE ‐ March 2015 Session 18875 RSH Consulting ‐ Robert S. Hansel RSH Consulting, Inc. is an IT security professional services firm established in 1992 and dedicated to helping clients strengthen their IBM z/OS mainframe access controls by fully exploiting all the capabilities and latest innovations in RACF. RSH's services include RACF security reviews and audits, initial implementation of new controls, enhancement and remediation of existing controls, and training. • www.rshconsulting.com • 617‐969‐9050 Robert S. Hansel is Lead RACF Specialist and founder of RSH Consulting, Inc. He began working with RACF in 1986 and has been a RACF administrator, manager, auditor, instructor, developer, and consultant. Mr. Hansel is especially skilled at redesigning and refining large‐scale implementations of RACF using role‐based access control concepts. He is a leading expert in securing z/OS Unix using RACF. Mr. Hansel has created elaborate automated tools to assist clients with RACF administration, database merging, identity management, and quality assurance. • 617‐969‐8211 • [email protected] • www.linkedin.com/in/roberthansel • http://twitter.com/RSH_RACF RACF Command Tips SHARE 2 © 2016 RSH Consulting, Inc. All Rights Reserved. March 2016 Topics . User Commands . Group Commands . Dataset Command . General Resource Commands . PERMIT Command . Generic Profile Refresh . List Commands . SEARCH Command . Console Command Entry . Building Commands with Microsoft Excel RACF and z/OS are Trademarks of the International Business Machines Corporation RACF Command Tips SHARE 3 © 2016 RSH Consulting, Inc. All Rights Reserved. March 2016 User Commands . ADDUSER Defaults: • OWNER ‐ Creator's ID • DFLTGRP ‐ Creator's Current Connect Group • PASSWORD ‐ Pre‐z/OS 2.2: Default Group z/OS 2.2: NOPASSWORD • Always specify when creating new ID . -
Implementing Nfsv4 in the Enterprise: Planning and Migration Strategies
Front cover Implementing NFSv4 in the Enterprise: Planning and Migration Strategies Planning and implementation examples for AFS and DFS migrations NFSv3 to NFSv4 migration examples NFSv4 updates in AIX 5L Version 5.3 with 5300-03 Recommended Maintenance Package Gene Curylo Richard Joltes Trishali Nayar Bob Oesterlin Aniket Patel ibm.com/redbooks International Technical Support Organization Implementing NFSv4 in the Enterprise: Planning and Migration Strategies December 2005 SG24-6657-00 Note: Before using this information and the product it supports, read the information in “Notices” on page xi. First Edition (December 2005) This edition applies to Version 5, Release 3, of IBM AIX 5L (product number 5765-G03). © Copyright International Business Machines Corporation 2005. All rights reserved. Note to U.S. Government Users Restricted Rights -- Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Contents Notices . xi Trademarks . xii Preface . xiii The team that wrote this redbook. xiv Acknowledgments . xv Become a published author . xvi Comments welcome. xvii Part 1. Introduction . 1 Chapter 1. Introduction. 3 1.1 Overview of enterprise file systems. 4 1.2 The migration landscape today . 5 1.3 Strategic and business context . 6 1.4 Why NFSv4? . 7 1.5 The rest of this book . 8 Chapter 2. Shared file system concepts and history. 11 2.1 Characteristics of enterprise file systems . 12 2.1.1 Replication . 12 2.1.2 Migration . 12 2.1.3 Federated namespace . 13 2.1.4 Caching . 13 2.2 Enterprise file system technologies. 13 2.2.1 Sun Network File System (NFS) . 13 2.2.2 Andrew File System (AFS) . -
Applying the ETL Process to Blockchain Data. Prospect and Findings
information Article Applying the ETL Process to Blockchain Data. Prospect and Findings Roberta Galici 1, Laura Ordile 1, Michele Marchesi 1 , Andrea Pinna 2,* and Roberto Tonelli 1 1 Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy; [email protected] (R.G.); [email protected] (L.O.); [email protected] (M.M.); [email protected] (R.T.) 2 Department of Electrical and Electronic Engineering (DIEE), University of Cagliari, Piazza D’Armi, 09100 Cagliari, Italy * Correspondence: [email protected] Received: 7 March 2020; Accepted: 7 April 2020; Published: 10 April 2020 Abstract: We present a novel strategy, based on the Extract, Transform and Load (ETL) process, to collect data from a blockchain, elaborate and make it available for further analysis. The study aims to satisfy the need for increasingly efficient data extraction strategies and effective representation methods for blockchain data. For this reason, we conceived a system to make scalable the process of blockchain data extraction and clustering, and to provide a SQL database which preserves the distinction between transaction and addresses. The proposed system satisfies the need to cluster addresses in entities, and the need to store the extracted data in a conventional database, making possible the data analysis by querying the database. In general, ETL processes allow the automation of the operation of data selection, data collection and data conditioning from a data warehouse, and produce output data in the best format for subsequent processing or for business. We focus on the Bitcoin blockchain transactions, which we organized in a relational database to distinguish between the input section and the output section of each transaction.