NSF Data Management Plan - Implications for LibrariansAndrew Sallans
A. Sallans. "NSF Data Management Plan - Implications for Librarians." Presented at the Science and Technology Section (STS) Hot Topics Discussion Group Meeting of the American Library Association's 2011 Midwinter Meeting. 8 January 2011
Improving Integrity, Transparency, and Reproducibility Through Connection of ...Andrew Sallans
The Center for Open Science (COS) was founded as a non-profit technology start-up in 2013 with the goal of improving transparency and reproducibility by connecting the scholarly workflow. COS achieves this goal through the development of a free, open source web application called the Open Science Framework (OSF), providing features like file sharing and citing, persistent urls, provenance tracking, and automated versioning. Initial workflow API connections focused on storage services and included Figshare, GitHub, Amazon S3, Dropbox, and Dataverse. The team is now working to connect other parts of the workflow with services like DMPTool, Databib/re3data, and Databrary. This session will introduce the core architecture and the problems that it solves, and illustrate how connecting services can benefit everyone involved in supporting the research ecosystem. COS is funded through the generosity of grants from the Laura and John Arnold Foundation, the John Templeton Foundation, the Alfred P. Sloan Foundation, the Association of Research Libraries, and others.
Presented at CNI Fall 2014, Washington, DC.
February 18 2014 NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Capacity Building: Leveraging existing library networks to take on research data
Heidi Imker, Director of the Research Data Service, University of Illinois at Urbana-Champaign
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Keynote Address: Data Management Plan Requirements at the US Department of Energy
Laura J. Biven, Ph.D., Senior Science and Technology Advisor, Office of the Deputy Director for Science Programs, Office of Science, US Department of Energy
NSF Data Management Plan - Implications for LibrariansAndrew Sallans
A. Sallans. "NSF Data Management Plan - Implications for Librarians." Presented at the Science and Technology Section (STS) Hot Topics Discussion Group Meeting of the American Library Association's 2011 Midwinter Meeting. 8 January 2011
Improving Integrity, Transparency, and Reproducibility Through Connection of ...Andrew Sallans
The Center for Open Science (COS) was founded as a non-profit technology start-up in 2013 with the goal of improving transparency and reproducibility by connecting the scholarly workflow. COS achieves this goal through the development of a free, open source web application called the Open Science Framework (OSF), providing features like file sharing and citing, persistent urls, provenance tracking, and automated versioning. Initial workflow API connections focused on storage services and included Figshare, GitHub, Amazon S3, Dropbox, and Dataverse. The team is now working to connect other parts of the workflow with services like DMPTool, Databib/re3data, and Databrary. This session will introduce the core architecture and the problems that it solves, and illustrate how connecting services can benefit everyone involved in supporting the research ecosystem. COS is funded through the generosity of grants from the Laura and John Arnold Foundation, the John Templeton Foundation, the Alfred P. Sloan Foundation, the Association of Research Libraries, and others.
Presented at CNI Fall 2014, Washington, DC.
February 18 2014 NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Capacity Building: Leveraging existing library networks to take on research data
Heidi Imker, Director of the Research Data Service, University of Illinois at Urbana-Champaign
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Keynote Address: Data Management Plan Requirements at the US Department of Energy
Laura J. Biven, Ph.D., Senior Science and Technology Advisor, Office of the Deputy Director for Science Programs, Office of Science, US Department of Energy
NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Enabling transparency and efficiency in the research landscape
Dr. Melissa Haendel, Associate Professor, Ontology Development Group, OHSU Library, Department of Medical Informatics and Epidemiology, Oregon Health & Science University
UVa Library Scientific Data Consulting Group (SciDaC): New Partnerships and...Andrew Sallans
A. Sallans. "UVa Library Scientific Data Consulting Group (SciDaC): New Partnerships and Services to Support Scientific Data in the Library." Presented at the 2011 International Association for Social Science Information Services and Technology.
RDAP13 Elizabeth Moss: The impact of data reuseASIS&T
Kathleen Fear, ICPSR, University of Michigan
“The impact of data reuse: a pilot study of 5 measures”
Panel: Data citation and altmetrics
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13
Research Data Access and Preservation Summit, 2014
San Diego, CA
March 26-28, 2014
Jared Lyle, ICPSR
Jennifer Doty, Emory University
Joel Herndon, Duke University
Libbie Stephenson, University of California, Los Angeles
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Learning to Curate Research Data
Jennifer Doty, Research Data Librarian, Emory Center for Digital Scholarship, Emory University, Robert W. Woodruff Library
Slides | Targeting the librarian’s role in research servicesLibrary_Connect
Slides from the Nov. 8, 2016 Library Connect webinar "Targeting the librarian’s role in research services" with Nina Exner, Amanda Horsman and Mark Reed. See the full webinar at: http://libraryconnect.elsevier.com/library-connect-webinars?commid=223121
Feb 26 NISO Training Thursday
Crafting a Scientific Data Management Plan
About the Training
Addressing a data management plan for the first time can be an intimidating exercise. Join NISO for a hands-on workshop that will guide you through the elements of creating a data management plan, including gathering necessary information, identifying needed resources, and navigating potential pitfalls. Participants explore the important components of a data management plan and critique excerpts of sample plans provided by the instructors.
This session is meant to be a guided, step-by-step session that will follow the February 18 NISO Virtual Conference, Scientific Data Management: Caring for Your Institution and its Intellectual Wealth.
About the Instructors
Kiyomi D. Deards, MSLIS, Assistant Professor, University of Nebraska-Lincoln Libraries
Jennifer Thoegersen, Data Curation Librarian, University of Nebraska-Lincoln Libraries
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Building Best Practices in Research Data Management: Tisch Library’s Initiatives
Regina F. Raboin, Science Research and Instruction Librarian/ Data Management Services Group Coordinator, Tisch Library, Tufts University
RDAP 15 Local ICPSR Data Curation Workshop Pilot ProjectASIS&T
Research Data Access and Preservation Summit, 2015
Minneapolis, MN
April 22-23, 2015
Linda Detterman, Jennifer Doty, Jared Lyle, Amy Pienta, Lizzy Rolando and Mandy Swygart-Hobaugh
February 18 2015 NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Network Effects: RMap Project
Sheila M. Morrissey, Senior Researcher, ITHAKA
Poster RDAP13: Data information literacy multiple paths to a single goalASIS&T
Jake Carlson, Jon Jeffryes, Brian Westra and Sarah Wright
Data Information Literacy: Multiple Paths to a Single Goal
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13
Slides | Research data literacy and the libraryColleen DeLory
Slides from the Dec. 8, 2016 Library Connect webinar "Research data literacy and the library" with Sarah Wright, Christian Lauersen and Anita de Waard. See the full webinar at: http://libraryconnect.elsevier.com/library-connect-webinars?commid=226043
Introduction to research data management; Lecture 01 for GRAD521Amanda Whitmire
Lesson 1: Introduction to research data management. From a series of lectures from a 10-week, 2-credit graduate-level course in research data management (GRAD521, offered at Oregon State University).
The course description is: "Careful examination of all aspects of research data management best practices. Designed to prepare students to exceed funder mandates for performance in data planning, documentation, preservation and sharing in an increasingly complex digital research environment. Open to students of all disciplines."
Major course content includes: Overview of research data management, definitions and best practices; Types, formats and stages of research data; Metadata (data documentation); Data storage, backup and security; Legal and ethical considerations of research data; Data sharing and reuse; Archiving and preservation.
See also, "Whitmire, Amanda (2014): GRAD 521 Research Data Management Lectures. figshare. http://dx.doi.org/10.6084/m9.figshare.1003835. Retrieved 23:25, Jan 07, 2015 (GMT)"
NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Enabling transparency and efficiency in the research landscape
Dr. Melissa Haendel, Associate Professor, Ontology Development Group, OHSU Library, Department of Medical Informatics and Epidemiology, Oregon Health & Science University
UVa Library Scientific Data Consulting Group (SciDaC): New Partnerships and...Andrew Sallans
A. Sallans. "UVa Library Scientific Data Consulting Group (SciDaC): New Partnerships and Services to Support Scientific Data in the Library." Presented at the 2011 International Association for Social Science Information Services and Technology.
RDAP13 Elizabeth Moss: The impact of data reuseASIS&T
Kathleen Fear, ICPSR, University of Michigan
“The impact of data reuse: a pilot study of 5 measures”
Panel: Data citation and altmetrics
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13
Research Data Access and Preservation Summit, 2014
San Diego, CA
March 26-28, 2014
Jared Lyle, ICPSR
Jennifer Doty, Emory University
Joel Herndon, Duke University
Libbie Stephenson, University of California, Los Angeles
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Learning to Curate Research Data
Jennifer Doty, Research Data Librarian, Emory Center for Digital Scholarship, Emory University, Robert W. Woodruff Library
Slides | Targeting the librarian’s role in research servicesLibrary_Connect
Slides from the Nov. 8, 2016 Library Connect webinar "Targeting the librarian’s role in research services" with Nina Exner, Amanda Horsman and Mark Reed. See the full webinar at: http://libraryconnect.elsevier.com/library-connect-webinars?commid=223121
Feb 26 NISO Training Thursday
Crafting a Scientific Data Management Plan
About the Training
Addressing a data management plan for the first time can be an intimidating exercise. Join NISO for a hands-on workshop that will guide you through the elements of creating a data management plan, including gathering necessary information, identifying needed resources, and navigating potential pitfalls. Participants explore the important components of a data management plan and critique excerpts of sample plans provided by the instructors.
This session is meant to be a guided, step-by-step session that will follow the February 18 NISO Virtual Conference, Scientific Data Management: Caring for Your Institution and its Intellectual Wealth.
About the Instructors
Kiyomi D. Deards, MSLIS, Assistant Professor, University of Nebraska-Lincoln Libraries
Jennifer Thoegersen, Data Curation Librarian, University of Nebraska-Lincoln Libraries
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Building Best Practices in Research Data Management: Tisch Library’s Initiatives
Regina F. Raboin, Science Research and Instruction Librarian/ Data Management Services Group Coordinator, Tisch Library, Tufts University
RDAP 15 Local ICPSR Data Curation Workshop Pilot ProjectASIS&T
Research Data Access and Preservation Summit, 2015
Minneapolis, MN
April 22-23, 2015
Linda Detterman, Jennifer Doty, Jared Lyle, Amy Pienta, Lizzy Rolando and Mandy Swygart-Hobaugh
February 18 2015 NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Network Effects: RMap Project
Sheila M. Morrissey, Senior Researcher, ITHAKA
Poster RDAP13: Data information literacy multiple paths to a single goalASIS&T
Jake Carlson, Jon Jeffryes, Brian Westra and Sarah Wright
Data Information Literacy: Multiple Paths to a Single Goal
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13
Slides | Research data literacy and the libraryColleen DeLory
Slides from the Dec. 8, 2016 Library Connect webinar "Research data literacy and the library" with Sarah Wright, Christian Lauersen and Anita de Waard. See the full webinar at: http://libraryconnect.elsevier.com/library-connect-webinars?commid=226043
Introduction to research data management; Lecture 01 for GRAD521Amanda Whitmire
Lesson 1: Introduction to research data management. From a series of lectures from a 10-week, 2-credit graduate-level course in research data management (GRAD521, offered at Oregon State University).
The course description is: "Careful examination of all aspects of research data management best practices. Designed to prepare students to exceed funder mandates for performance in data planning, documentation, preservation and sharing in an increasingly complex digital research environment. Open to students of all disciplines."
Major course content includes: Overview of research data management, definitions and best practices; Types, formats and stages of research data; Metadata (data documentation); Data storage, backup and security; Legal and ethical considerations of research data; Data sharing and reuse; Archiving and preservation.
See also, "Whitmire, Amanda (2014): GRAD 521 Research Data Management Lectures. figshare. http://dx.doi.org/10.6084/m9.figshare.1003835. Retrieved 23:25, Jan 07, 2015 (GMT)"
Studie zur Personalentwicklung - TeamfitPeter Wolff
Das vergessene Auswahlkriterium bei Personalauswahl und Teambuilding | Management Summary | WolffPartners in Kooperation mit heiden Associates aus 12/2009
Having Total 2 year 4 months of working experience with J2SE, J2EE, Java-script ,Jquery ,ajax , Spring, Hibernate,Oracle, Mysql in 3-Tier based Applications. currently working from December 2014 as Software Developer with innologix consulting pvt.ltd.
Development Challenges, South-South Solutions: November 2011 IssueDavid South Consulting
Development Challenges, South-South Solutions is the monthly e-newsletter for the United Nations Development Programme’s South-South Cooperation Unit (www.southerninnovator.org). It has been published every month since 2006.
ISSN 2227-3905
Stories by David South
Design and Layout: Sólveig Rolfsdóttir, UNDP South-South Cooperation Unit
Contact the Unit (http://ssc.undp.org/content/ssc.html) to receive a copy of the new global magazine Southern Innovator. Issue 1 is out now and about innovators in mobile phone and information technologies.
Follow @SouthSouth1
In this issue:
New African Film Proving Power of Creative Economy
Recycling Waste to Boost Incomes and Opportunities
Virtual Supermarket Shopping Takes off in China
Bolivia Grabs World Media Attention with Salt Hotel
PRESENTACIÓN: DIPLOMADO: INNOVACIÓN EMPRESARIAL 2.0 PARA EMPRENDEDORESOLIVIER SOUMAH-MIS
Diplomado para emprendedores que quieren crear una empresa del siglo XXI implementando estrategias digitales con herramientas 2.0 y recursos social media.
Catálogo de camisetas promocionales de la marca Roly temporada 2012. Sudaderas, polos, camisetas, pantalones, chaquetas, etc. Impresión y personalización mediante serigrafía, sublimación o bordados.
Scholars and researchers are being asked by an increasing number of research sponsors and journals to outline how they will manage and share their research data. This is an introduction to data management and sharing practices with some specific information for Columbia University researchers.
Presenters : Libbie Stephenson, Jared Lyle
This session discusses the value of and methods for curating data, especially in light of recent government and academic initiatives. Special attention will be paid to data management plans.
Researchers: how and why manage research data; CDU Darwin 070915Richard Ferrers
An ANDS(.org.au) brief presentation to Charles Darwin University researchers on research data management (RDM). What, Why and How to do RDM? Presentation 07 Sept 2015, Darwin Aust.
Research Data Management: Part 1, Principles & ResponsibilitiesAmyLN
This two-part course is a collaboration between CU Libraries/Information Services and the Office of Research Compliance & Training. The purpose of this course is to familiarize you with the various aspects of research data management (RDM)
Part 1: Why RDM is both recommended and required
What research data are
Who is responsible for RDM
Part 2:
When RDM activities occur
How you can carry out RDM activities
DataONE Education Module 01: Why Data Management?DataONE
Lesson 1 in a set of 10 created by DataONE on Best Practices fo Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
Presentation given by Sarah Jones at a seminar run by LSHTM on 6th November 2012. http://www.lshtm.ac.uk/newsevents/events/2012/11/developing-data-management-expertise-in-research---half-day-event
Data Management for Research (New Faculty Orientation)aaroncollie
Situates research data management as a contingency that should be addressed and provisioned for during planning and research design. Draws out fundamental practices for file management, data description, and enumerates storage decision points.
NIH Data Policy or: How I Learned to Stop Worrying and Love the Data Manageme...Kristin Briney
This talk provides background information on the NIH policy, what it is and how it came to be. It then goes through how to create a data sharing plan on your own and using the DMPTool. The talk wraps up with my top 5 recommendations for data management for those who have never done data management before.
Introduction to research data management. Presented by Natasha Simons at the C3DIS post conference workshop: Managed data – trusted research: an introduction to Research Data Management, Melbourne 31st may 2018
Similar to Hands-On Data Management Planning for Life Sciences (20)
Open Science Framework (OSF): Presentation and TrainingAndrew Sallans
Presentation Date: December 12, 2013.
Location: UC Berkeley, CA
Presenters: Johanna Cohoon & Andrew Sallans (Center for Open Science)
Center for Open Science website: http://centerforopenscience.org
Berkeley Initiative for Transparency in the Social Sciences website: http://bitss.org/annual-meeting/2013-2/
A. Sallans. "Practical Applications of e-Science." Presented at the 2011 eScience Bootcamp at the University of Virginia's Claude Moore Health Sciences Library. 4 March 2011
Understanding the Big Picture of e-ScienceAndrew Sallans
A. Sallans. "Understanding the Big Picture of e-Science." Presented at the 2011 eScience Bootcamp at the University of Virginia's Claude Moore Health Sciences Library. 4 March 2011
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Hands-On Data Management Planning for Life Sciences
1. Hands-‐On
Data
Management
Planning
for
Life
Sciences
Andrew
Sallans
Andrea
Denton
Head
of
Strategic
Data
Ini5a5ves
Research
and
Data
Services
Manager
University
of
Virginia
Library
Claude
Moore
Health
Sciences
Library
als9q@virginia.edu
ash6b@virginia.edu
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
2. Goals
for
the
workshop
• Learn
about
data
management
planning
• Learn
about
available
resources
• Develop
rough
draU
of
a
data
management
plan
for
a
grant
• Gain
peer
and
expert
feedback
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
3. Why
should
you
care
about
data
management
planning?
• It’s
good
science:
reproducible
results
and
con5nuity
• Transparency
and
accountability
• Gain
a
compe55ve
edge
in
grant
compe55on
• Get
credit
by
making
your
data
citable,
more
impact
• Be
efficient
and
avoid
data
loss
• It’s
complex
and
requires
aPen5on
to
many
parts
• You
may
be
required
to
by
your
government,
funder,
ins5tu5on,
publishers,
etc.
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
4. Why
not?
h"p://memegenerator.net/Fist-‐Pump-‐Baby
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
5. Recent
news
• White
House,
Office
of
Science
and
Technology
Policy
from
February
22,
2013
• Federal
research
agencies
funding
more
than
$100M/
year
must
develop
plan
to
make
the
results
(papers
and
data)
of
federally
funded
research
available
to
the
public
within
one
year
of
publica5on
• Also
requires
researchers
to
bePer
account
for
and
manage
data
h"p://www.whitehouse.gov/sites/default/files/microsites/ostp/
ostp_public_access_memo_2013.pdf
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
6. Example:
Na5onal
Science
Founda5on
– Data
Sharing
Policy:
Awards
&
Administra5on
Guide
Chapter
IV.D.4
– Data
Management
Plan
requirement:
Grant
Proposal
Guide
Chapter
II.C.2.j
– Addi5onal
requirements
from
individual
Directorates
and
Divisions
(e.g.,
BIO,
CISE,
EHR,
GEO,
MPS,
SBE):
Dissemina5on
and
Sharing
of
Results
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
7. Caveat:
it’s
not
just
the
NSF
CDC
NEH
DOE
NIH
Read
calls
for
proposals
carefully
CRkfs73p
and
ask
program
director
about
EPA
USDA
specific
data
management
IMLS
Private
and
public
founda5ons
requirements.
Build
5me
into
your
NASA
Many
research
funding
agencies
proposal
development
to
in
the
U.K.,
Australia,
and
other
formulate
a
data
management
countries
plan!
NOAA
Etc…
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
8. What
is
a
Data
Management
Plan?
• A
comprehensive
plan
of
how
you
will
manage
your
research
data
throughout
the
lifecycle
of
your
research
project
AND
• Brief
descrip5on
of
how
you
will
comply
with
funder’s
data
sharing
policy
• Reviewed
as
part
of
a
grant
applica5on
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
9. Dissemina=on
&
Sharing
of
Research
Results
“Inves5gators
are
expected
to
share
with
other
researchers,
at
no
more
than
incremental
cost
and
within
a
reasonable
5me,
the
primary
data,
samples,
physical
collec5ons
and
other
suppor5ng
materials
created
or
gathered
in
the
course
of
work
under
NSF
grants.
Grantees
are
expected
to
encourage
and
facilitate
such
sharing.”
Na=onal
Science
Founda=on:
Award
&
AdministraGon
Guide
(AAG)
Chapter
VI.D.4
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
10. Plan
for
Data
Management
&
Sharing
of
the
Products
of
Research
As
of
January
18,
2011:
“Proposals
must
include
a
supplementary
document
of
no
more
than
two
pages
labeled
“Data
Management
Plan”.
This
supplement
should
describe
how
the
proposal
will
conform
to
NSF
policy
on
the
dissemina5on
and
sharing
of
research
results,
and
may
include…...”
NSF:
Grant
Proposal
Guide
(GPG)
Chapter
II.C.2.j
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
11. Which
NSF
requirement
to
use?
• Which
Guideline
Should
I
follow?
§ First:
follow
the
requirements
laid
out
in
the
specific
solicita5on,
if
any.
§ Second:
follow
the
guidelines
published
by
the
appropriate
NSF
directorate
and/or
division.
If
there
is
a
conflict,
the
laPer
takes
precedence.
§ Third:
follow
the
more
general
guidelines.
• Interdisciplinary
Proposals
§ Use
guidelines
appropriate
to
the
lead
program
(if
there
are
specific
guidelines)
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
12. Parts
of
a
(Generic)
NSF
Data
Management
Plan
I. Products
of
the
Research:
The
types
of
data,
samples,
physical
collec5ons,
soUware,
curriculum
materials,
and
other
materials
to
be
produced
in
the
course
of
the
project.
II. Data
Formats:
The
standards
to
be
used
for
data
and
metadata
format
and
content
(where
exis5ng
standards
are
absent
or
deemed
inadequate,
this
should
be
documented
along
with
any
proposed
solu5ons
or
remedies).
III. Access
to
Data
and
Data
Sharing
Prac=ces
and
Policies:
Policies
for
access
and
sharing
including
provisions
for
appropriate
protec5on
of
privacy,
confiden5ality,
security,
intellectual
property,
or
other
rights
or
requirements.
IV. Policies
for
Re-‐Use,
Re-‐Distribu=on,
and
Produc=on
of
Deriva=ves.
V. Archiving
of
Data:
Plans
for
archiving
data,
samples,
and
other
research
products,
and
for
preserva5on
of
access
to
them.
Grant
Proposal
Guide
(GPG)
Chapter
II.C.2.j
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
13. I.
Types
of
Data
• Ques=ons
to
answer:
§ What
data
will
be
generated
in
the
research?
§ What
data
types
will
you
be
crea5ng
or
capturing?
§ How/when/where
will
you
capture
or
create
the
data?
§ How
will
the
data
be
processed?
§ If
you
will
be
using
exis5ng
data,
state
that
fact
and
include
where
you
got
it.
What
is
the
rela5onship
between
the
data
you
are
collec5ng
and
the
exis5ng
data?
13
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
14. II.
Data
and
Metadata
Standards
• Ques=ons
to
answer:
§ Which
file
formats
will
you
use
for
your
data,
and
why?
§ What
form
will
metadata
describing/
documen5ng
your
data
take?
§ How
will
you
create
or
capture
these
details?
§ Which
metadata
standards
will
you
use
and
why
have
you
chosen
them?
§ What
contextual
details
(metadata)
are
needed
to
make
the
data
you
capture
or
collect
meaningful?
14
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
15. III.
Policies
for
Access
and
Sharing
&
Provisions
for
Appropriate
Protec=on/Privacy
• Ques=ons
to
answer:
§ How/when
will
you
make
the
data
available?
§ What
is
the
process
for
gaining
access
to
the
data?
§ Does
the
original
data
collector/creator/principal
inves5gator
retain
the
right
to
use
the
data
before
opening
it
up
to
wider
use?
§ Are
there
any
embargo
periods
for
poli5cal/
commercial/
patent
reasons?
If
so,
give
details.
15
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
16. III.
Policies
for
Access
and
Sharing
&
Provisions
for
Appropriate
Protec=on/Privacy
(Cont.)
• More
Ques=ons
to
answer:
§ Are
there
ethical
and
privacy
issues?
If
so,
how
will
these
be
resolved?
§ What
have
you
done
to
comply
with
your
obliga5ons
in
your
IRB
Protocol?
§ Who
will
hold
the
intellectual
property
rights
to
the
data
and
how
might
this
affect
data
access?
16
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
17. IV.
Policies
and
Provisions
for
Re-‐Use,
Re-‐Distribu=on
• Ques=ons
to
answer:
§ Will
any
permission
restric5ons
need
to
be
placed
on
the
data?
§ Which
bodies/groups
are
likely
to
be
interested
in
the
data?
§ What
could
be
the
intended
uses
of
the
data?
17
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
18. V.
Plans
for
Archiving
and
Preserva=on
of
Access
• Ques=ons
to
answer:
§ What
data
will
be
preserved
for
the
long-‐term?
§ What
is
the
long-‐term
strategy
for
maintaining,
cura5ng
and
archiving
the
data?
§ Which
archive/repository/database
have
you
iden5fied
as
a
place
to
deposit
data?
§ What
procedures
does
your
intended
long-‐term
data
storage
facility
have
in
place
for
preserva5on
and
backup?
§ How
long
will/should
data
be
kept
beyond
the
life
of
the
project?
18
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
19. V.
Plans
for
Archiving
and
Preserva=on
of
Access
(Cont.)
• More
Ques=ons
to
answer:
§ What
transforma5ons
will
be
necessary
to
prepare
data
for
preserva5on
/
data
sharing?
§ What
metadata/
documenta5on
will
be
submiPed
alongside
the
data
or
created
on
deposit/
transforma5on
in
order
to
make
the
data
reusable?
§ What
related
informa5on
will
be
deposited?
19
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
20. What
needs
to
be
in
a
data
management
plan
for
a
grant?
Example:
NSF
• Two
pages
long
• Reviewed
for
merit
and/or
impact
with
proposal
• Your
plan
should
minimally
address
five
points:
– Data
being
produced
– Format
and
descrip5on
– Access
and
sharing
– Reuse
– Archiving
Be
sure
to
address
addi5onal
Directorate
or
Division
guidelines
and
specific
program
requirements!
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
21. Three
Data
Management
Planning
Resources
• DMPTool,
hPp://dmptool.org
–
Helps
you
create
a
data
management
plan
to
meet
grant
requirements
and
iden5fy
UVA
support
resources
and
policies
• Databib,
hPp://databib.org
–
Helps
you
find
an
appropriate
place
to
deposit
your
data
• Libra,
hPp://libra.virginia.edu
-‐
Helps
UVA
faculty,
graduate
students,
and
staff
by
providing
a
place
to
deposit
and
share
datasets
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
22. h`p://dmptool.org
Step-‐by-‐step
wizard
for
genera5ng
DMP
Create
|
edit
|
re-‐use
|
share
|
save
|
generate
Open
to
community
Links
to
institutional
resources
Directorate
information
&
updates
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
23. Goals
of
the
DMPTool
I. To
provide
researchers
a
simple
way
to
create
a
DMP
for
their
funding
agency
• Ques5ons
asked
by
the
agency
• Addi5onal
explana5on/context
provided
by
the
agency
• Links
to
the
agency
website
for
policies,
help,
guidance
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
24. Goals
of
the
DMPTool
II. To
provide
researchers
with
DMP
informa=on
from
their
home
ins=tu=on
• Resources
and
services
to
help
them
manage
data
• Help
text
for
specific
ques5ons
• Suggested
answers
to
ques5ons;
easy
to
cut-‐N-‐paste
• News
&
events
related
to
data
management
on
campus
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
25. Last
point:
Grant
requirements
versus
ideal
• Grant
Driven
– Requirements
– Sharing
and
public
access
to
research
• Opera5onal
– Research
con5nuity
– Avoiding
data
loss
– Efficiency
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
26. Team
Exercise
30
minutes
1. Iden5fy
a
grant
that
you
have
or
might
apply
for.
2. Locate
the
requirements
for
that
grant
in
the
DMPTool.
3. Go
through
plan
sec5ons
in
DMPTool
workflow
to
produce
draU
plan.
– Be
sure
to
address
metadata,
access
policies,
repositories
.
4. Iden5fy
solu5ons
and
available
support
through
DMPTool
sec5ons
or
ask
for
guidance.
5. Record
issues
and
ques5ons
for
discussion.
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
27. Presenta5on
of
DraU
DMPs
15
minutes
• Iden5fy
grant
• Describe
project
briefly
• Explain
requirements
• Describe
planned
solu5ons
– Must
address
metadata,
access
policies,
and
repositories.
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
28. Ques5ons
and
Discussion?
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.
29. Follow-‐up
• Contact
the
Scien5fic
Data
Consul5ng
Group
for
help
with
DMP
prepara5on
– Grant
driven:
hPp://www2.lib.virginia.edu/brown/data/
DMP_Support.html
– Opera5onal
• Email:
scidac@virginia.edu
Crea5ve
Commons
License
”Hands-‐On
Data
Management
Planning
for
Life
Sciences",
3/19/13
by
Andrew
L.
Sallans
is
licensed
under
a
Crea5ve
Commons
APribu5on-‐ShareAlike
3.0
Unported
License.