Layne Johnson "e-Science at the Univeristy of Minnesota"The TMC Library
The document summarizes a presentation on e-science given at the University of Minnesota. It discusses how technological advances have increased data generation in science, requiring greater computing resources and collaboration. It describes the university's experiences developing e-science support initiatives through its libraries, such as providing data management planning assistance and research networking tools. It also outlines future opportunities for libraries to further support e-science needs on issues like privacy, security, and scientists' data needs.
JEC Composites show Paris 2011 Intelligence ReportViedoc
As it has been the case for many years now, the JEC Composite Show Paris 2011 has once again gathered key players in the sector of the composite materials and has given opportunities to many companies to showcase new products and innovations. The automotive sector was much represented as carbon fiber reinforced plastics answer the growing need for more efficient and lighter vehicles. On the vegetal fiber side, flax fibers are gaining ground as we now see technical applications where linen replaces advantageously glass fiber. ...
Best practices of social media records policies ct sig - 03-31-11 (3)Claude Super
The document summarizes a study on best practices for social media records policies at 10 government agencies. It found that while active social media use has identified challenges for recordkeeping, agencies have also developed some best practices. Key challenges included defining social media content as records and capturing/tagging public content. Best practices involved communication between teams, using resources to develop policies, defining roles, and integrating social media into records management systems. The study makes recommendations to vendors and NARA to help address the issues.
The document provides information about the United States of America including its geography, government, population, currency, and natural resources. It then discusses some of the major cities in the US like Washington D.C., New York, Las Vegas, and Los Angeles as well as popular tourist destinations and cuisine. Washington D.C. is the capital with a population of around 617,996 people and is governed by Congress. New York has over 8 million residents and was the site of the 9/11 attacks. Las Vegas is known for its casinos and gambling. Florida's most visited site is Disney World located in Orlando.
The document describes the design and prototyping of a heavy lift octocopter. An octocopter uses 8 propellers arranged in 4 pairs of coaxial propellers to maximize lift. The design was optimized for an ASME student design competition rewarding payloads lifted. A coaxial design doubles lift compared to single propellers. The frame and protective shroud were manually constructed and components were chosen according to the custom design, with no preassembled kits used. The goal was to design for maximum lift within size requirements to earn the most points by lifting the heaviest payload.
Com 150 uop tutorials,com 150 uop assignments,com 150 uop entire classuniversity of phoenix
This document provides instructions for COM 150 students to complete their Week 9 Final Project, which involves writing a 1,500-1,750 word expository essay on the topic of cosmetic surgery. Students are directed to include a title page, introduction, thesis statement, body with citations, conclusion, and reference list. They must also submit a completed peer review form and have their paper reviewed using the plagiarism checker and Center for Writing Excellence. The document contains multiple links to tutorial sites providing guidance on the various COM 150 assignments.
Layne Johnson "e-Science at the Univeristy of Minnesota"The TMC Library
The document summarizes a presentation on e-science given at the University of Minnesota. It discusses how technological advances have increased data generation in science, requiring greater computing resources and collaboration. It describes the university's experiences developing e-science support initiatives through its libraries, such as providing data management planning assistance and research networking tools. It also outlines future opportunities for libraries to further support e-science needs on issues like privacy, security, and scientists' data needs.
JEC Composites show Paris 2011 Intelligence ReportViedoc
As it has been the case for many years now, the JEC Composite Show Paris 2011 has once again gathered key players in the sector of the composite materials and has given opportunities to many companies to showcase new products and innovations. The automotive sector was much represented as carbon fiber reinforced plastics answer the growing need for more efficient and lighter vehicles. On the vegetal fiber side, flax fibers are gaining ground as we now see technical applications where linen replaces advantageously glass fiber. ...
Best practices of social media records policies ct sig - 03-31-11 (3)Claude Super
The document summarizes a study on best practices for social media records policies at 10 government agencies. It found that while active social media use has identified challenges for recordkeeping, agencies have also developed some best practices. Key challenges included defining social media content as records and capturing/tagging public content. Best practices involved communication between teams, using resources to develop policies, defining roles, and integrating social media into records management systems. The study makes recommendations to vendors and NARA to help address the issues.
The document provides information about the United States of America including its geography, government, population, currency, and natural resources. It then discusses some of the major cities in the US like Washington D.C., New York, Las Vegas, and Los Angeles as well as popular tourist destinations and cuisine. Washington D.C. is the capital with a population of around 617,996 people and is governed by Congress. New York has over 8 million residents and was the site of the 9/11 attacks. Las Vegas is known for its casinos and gambling. Florida's most visited site is Disney World located in Orlando.
The document describes the design and prototyping of a heavy lift octocopter. An octocopter uses 8 propellers arranged in 4 pairs of coaxial propellers to maximize lift. The design was optimized for an ASME student design competition rewarding payloads lifted. A coaxial design doubles lift compared to single propellers. The frame and protective shroud were manually constructed and components were chosen according to the custom design, with no preassembled kits used. The goal was to design for maximum lift within size requirements to earn the most points by lifting the heaviest payload.
Com 150 uop tutorials,com 150 uop assignments,com 150 uop entire classuniversity of phoenix
This document provides instructions for COM 150 students to complete their Week 9 Final Project, which involves writing a 1,500-1,750 word expository essay on the topic of cosmetic surgery. Students are directed to include a title page, introduction, thesis statement, body with citations, conclusion, and reference list. They must also submit a completed peer review form and have their paper reviewed using the plagiarism checker and Center for Writing Excellence. The document contains multiple links to tutorial sites providing guidance on the various COM 150 assignments.
This study monitored ranavirus-associated mortality in amphibians in a Dutch heathland area in the year following a 2010 outbreak that killed thousands of common midwife toads and dozens of smooth newts. The aims were to document spatial and temporal patterns of ranavirus disease and identify potential enhancing factors. A fixed monitoring protocol was established for ponds in the area. The virus was found to be continuously present in the outbreak pond from 2010 and also present in surrounding ponds, causing ongoing mortality in all life stages of amphibians. However, no clear triggering factors for the outbreaks or continued mortality were identified.
Streamlining the Client's Workflows (in Joomla)Randy Carey
When our client or their staff login to manage their site and content, they have specific tasks in mind. This presentation demonstrates how we can identify these tasks and develop each into an intuitive set of streamlined steps. We will be examining ways to reduce the number of steps, reduce clutter, and make the entire process intuitive for our client.
The document discusses the rise of crowdfunding and how it has disrupted and disintermediated traditional financial intermediaries. It describes how early crowdfunding platforms in the 2000s allowed people to donate or invest small amounts in projects and receive rewards. This led to the growth of major crowdfunding sites like Kickstarter and peer-to-peer lending platforms like LendingClub and Kiva. The document argues that crowdfunding has made it possible to access capital outside of traditional sources and will continue growing as a new funding mechanism, similar to how eBay and Amazon disrupted retail markets.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
The document discusses the history and patterns of technological innovation and adoption. It argues that new technologies are first adopted for safety and military purposes, then expanded by businesses for economic prosperity, and finally taken up socially. This follows Abraham Maslow's hierarchy of needs, where individuals prioritize survival and security before socialization. The document uses examples like the telegraph, radio, and internet to show how each was initially developed for defense or business before being widely adopted for social purposes. It advises businesses to understand these priorities and patterns in order to best serve customers.
Motorola Solutions: Using Your Data to Create Safer CitiesMotorola Solutions
Data is growing more important to public safety today - 89% of public safety decision makers believe data is just as mission critical as voice. See how Motorola Solutions integrated command and control connects you to the communities you serve.
Learn more at www.motorolasolutions.com/safercities.
This is a multi-paged listing of Companies with their website URL's and other Miscellaneous websites for job and career search "job descriptions" and "job postings" for job candidates to search for jobs.
The document outlines an introduction to sustainability and greentech revolutions program run by SiliconFrench in 2007. The program covers topics like large companies adopting sustainability, sustainable startups, and a global perspective on sustainability. It discusses businesses, governments, research, and individuals taking action on sustainability. Examples highlighted include Whole Foods' sustainable practices and Tesla Motors' electric vehicles. The goal is to discuss business opportunities in clean technology and how sustainability can provide competitive advantages for companies.
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The document discusses the development of data services to support eScience/eResearch. It provides an overview of eScience, including that it involves large-scale collaborative science enabled by the internet using digital data. Characteristics of eScience include being data-driven, distributed, collaborative, and trans-disciplinary. Libraries are important to eScience because it involves large data sets, collections, and repositories. The document also discusses how science paradigms have shifted to become more computational and data-focused.
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This document discusses partnering for research data and the various stakeholders involved. It identifies key stakeholder roles like directors, librarians, repository managers, and research support offices. Infrastructure requirements for delivering data management services are outlined, including tools for data plans, tracking impact, and more. There is a skills gap around research data that institutions are working to address through training and new specialist librarian roles in areas like data curation and management. International collaboration could help promote data literacy.
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This document provides an overview of a professional development day for librarians on scientific data management. The day includes presentations on e-science, cyberinfrastructure, and data; a case study on data management for gravitational wave research; and a group activity to develop data management initiatives. The presentations will cover characteristics of e-science such as large collaborative digital datasets, and implications for libraries, including initiatives to provide data support services and address challenges in data preservation, access, and the research data lifecycle.
About the Webinar
Big data is being collected at a rate that is surpassing traditional analytical methods due to the constantly expanding ways in which data can be created and mined. Faculty in all disciplines are increasingly creating and/or incorporating big data into their research and institutions are creating repositories and other tools to manage it all. There are many challenge to effectively manage and curate this data—challenges that are both similar and different to managing document archives. Libraries can and are assuming a key role in making this information more useful, visible, and accessible, such as creating taxonomies, designing metadata schemes, and systematizing retrieval methods.
Our panelists will talk about their experience with big data curation, best practices for research data management, and the tools used by libraries as they take on this evolving role.
This document discusses the need to redefine information literacy frameworks to incorporate data literacy for the 21st century. It provides context on the growth of data-driven research and debates around roles in data management. It examines conceptions of data literacy from social science and science perspectives and examples of libraries developing data services. Finally, it analyzes pedagogical approaches to teaching data literacy and calls for discussion on integrating data literacy into information literacy frameworks and education.
Data Science and What It Means to Library and Information ScienceJian Qin
Data science involves collecting, analyzing, and preserving large datasets to extract knowledge and make predictions. It differs from traditional disciplines by dealing with heterogeneous, unstructured data from complex networks. A data scientist requires math, computing, communication skills, and the ability to ask the right questions. Libraries are well-positioned to offer various data services including data discovery, consulting, mining, integration, and curation to support research and decision-making. Practicing data science in libraries requires vision, risk-taking, data science knowledge, careful planning, and collaboration.
This study monitored ranavirus-associated mortality in amphibians in a Dutch heathland area in the year following a 2010 outbreak that killed thousands of common midwife toads and dozens of smooth newts. The aims were to document spatial and temporal patterns of ranavirus disease and identify potential enhancing factors. A fixed monitoring protocol was established for ponds in the area. The virus was found to be continuously present in the outbreak pond from 2010 and also present in surrounding ponds, causing ongoing mortality in all life stages of amphibians. However, no clear triggering factors for the outbreaks or continued mortality were identified.
Streamlining the Client's Workflows (in Joomla)Randy Carey
When our client or their staff login to manage their site and content, they have specific tasks in mind. This presentation demonstrates how we can identify these tasks and develop each into an intuitive set of streamlined steps. We will be examining ways to reduce the number of steps, reduce clutter, and make the entire process intuitive for our client.
The document discusses the rise of crowdfunding and how it has disrupted and disintermediated traditional financial intermediaries. It describes how early crowdfunding platforms in the 2000s allowed people to donate or invest small amounts in projects and receive rewards. This led to the growth of major crowdfunding sites like Kickstarter and peer-to-peer lending platforms like LendingClub and Kiva. The document argues that crowdfunding has made it possible to access capital outside of traditional sources and will continue growing as a new funding mechanism, similar to how eBay and Amazon disrupted retail markets.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
The document discusses the history and patterns of technological innovation and adoption. It argues that new technologies are first adopted for safety and military purposes, then expanded by businesses for economic prosperity, and finally taken up socially. This follows Abraham Maslow's hierarchy of needs, where individuals prioritize survival and security before socialization. The document uses examples like the telegraph, radio, and internet to show how each was initially developed for defense or business before being widely adopted for social purposes. It advises businesses to understand these priorities and patterns in order to best serve customers.
Motorola Solutions: Using Your Data to Create Safer CitiesMotorola Solutions
Data is growing more important to public safety today - 89% of public safety decision makers believe data is just as mission critical as voice. See how Motorola Solutions integrated command and control connects you to the communities you serve.
Learn more at www.motorolasolutions.com/safercities.
This is a multi-paged listing of Companies with their website URL's and other Miscellaneous websites for job and career search "job descriptions" and "job postings" for job candidates to search for jobs.
The document outlines an introduction to sustainability and greentech revolutions program run by SiliconFrench in 2007. The program covers topics like large companies adopting sustainability, sustainable startups, and a global perspective on sustainability. It discusses businesses, governments, research, and individuals taking action on sustainability. Examples highlighted include Whole Foods' sustainable practices and Tesla Motors' electric vehicles. The goal is to discuss business opportunities in clean technology and how sustainability can provide competitive advantages for companies.
BAZELEY - USCG-AUX Public Affairs Presentation PDF,
Leadership Team Training for serving stakeholders and the public in promoting membership opportunities and promoting the various missions of the USCG Auxiliary in all areas of service from Vessel Exams, Educating the Public in Safe Boating Practices, to Marine Safety and Incident Response activities.
Developing Data Services to Support eScience/eResearchJian Qin
The document discusses the development of data services to support eScience/eResearch. It provides an overview of eScience, including that it involves large-scale collaborative science enabled by the internet using digital data. Characteristics of eScience include being data-driven, distributed, collaborative, and trans-disciplinary. Libraries are important to eScience because it involves large data sets, collections, and repositories. The document also discusses how science paradigms have shifted to become more computational and data-focused.
Data Science: An Emerging Field for Future JobsJian Qin
Data deluge has become a reality in today's scientific research. What does it mean to future science workforce? How can you prepare yourself to embrace the data challenges and opportunities? This presentation will provide you with an overview of data science and what it means to you as future researchers and career scientists.
This document discusses partnering for research data and the various stakeholders involved. It identifies key stakeholder roles like directors, librarians, repository managers, and research support offices. Infrastructure requirements for delivering data management services are outlined, including tools for data plans, tracking impact, and more. There is a skills gap around research data that institutions are working to address through training and new specialist librarian roles in areas like data curation and management. International collaboration could help promote data literacy.
These are the slides for Robert H. McDonald for the Future Trends Panel Presentation at the the Inter-institutional Approaches to Supporting Scholarly Communication Symposium held on August 16, 2012 at the Georgia Institute of Technology.
RDAP13 Jared Lyle: Domain Repositories and Institutional Repositories Partn…ASIS&T
The document discusses opportunities for partnerships between domain repositories and institutional repositories to enhance data curation. It provides examples of how they can partner to 1) archive and share data more widely to avoid duplicate data collection, 2) improve data documentation and address disclosure issues to make data more useful for future researchers, and 3) provide tools and expertise around data processing, confidentiality review, and access to help researchers use the data while protecting privacy. Productive partnerships require selection of pilot projects, investment of time, and willingness to take on new roles from both sides.
This document provides an overview of a professional development day for librarians on scientific data management. The day includes presentations on e-science, cyberinfrastructure, and data; a case study on data management for gravitational wave research; and a group activity to develop data management initiatives. The presentations will cover characteristics of e-science such as large collaborative digital datasets, and implications for libraries, including initiatives to provide data support services and address challenges in data preservation, access, and the research data lifecycle.
About the Webinar
Big data is being collected at a rate that is surpassing traditional analytical methods due to the constantly expanding ways in which data can be created and mined. Faculty in all disciplines are increasingly creating and/or incorporating big data into their research and institutions are creating repositories and other tools to manage it all. There are many challenge to effectively manage and curate this data—challenges that are both similar and different to managing document archives. Libraries can and are assuming a key role in making this information more useful, visible, and accessible, such as creating taxonomies, designing metadata schemes, and systematizing retrieval methods.
Our panelists will talk about their experience with big data curation, best practices for research data management, and the tools used by libraries as they take on this evolving role.
This document discusses the need to redefine information literacy frameworks to incorporate data literacy for the 21st century. It provides context on the growth of data-driven research and debates around roles in data management. It examines conceptions of data literacy from social science and science perspectives and examples of libraries developing data services. Finally, it analyzes pedagogical approaches to teaching data literacy and calls for discussion on integrating data literacy into information literacy frameworks and education.
Data Science and What It Means to Library and Information ScienceJian Qin
Data science involves collecting, analyzing, and preserving large datasets to extract knowledge and make predictions. It differs from traditional disciplines by dealing with heterogeneous, unstructured data from complex networks. A data scientist requires math, computing, communication skills, and the ability to ask the right questions. Libraries are well-positioned to offer various data services including data discovery, consulting, mining, integration, and curation to support research and decision-making. Practicing data science in libraries requires vision, risk-taking, data science knowledge, careful planning, and collaboration.
Sarah Jones RDM from a disciplinary perspectiveJisc
This document discusses research data management from a disciplinary perspective. It begins with an overview of case studies on disciplinary practice from various sources. It then groups disciplines into Arts & Humanities, Social Sciences, Sciences & Engineering, and Life Sciences. For each group, it discusses common practices, challenges, and examples. It also discusses a research data typology commissioned by RLUK to help librarians understand researchers' data needs and types of data across disciplines. Overall, the document provides a high-level overview of differences in research data management practices across broad disciplinary categories.
The document provides logistics for a webinar on data curation profiles and the DMPTool. It includes instructions for calling into the audio, asking questions in the chat, and finding recordings and slides. The webinar will discuss the history of data curation profiles, comparing them to data management plans, and a case study of using data curation profiles. Data curation profiles involve interviewing researchers about their data practices and needs in order to understand how to support them, while data management plans focus on requirements for funding. Both tools can help librarians engage with researchers, though data curation profiles provide a more in-depth understanding of researchers' full data lifecycles.
How to develop a Pilot Data Management Infrastructure for Biomedical Research...Meik Poschen
This presentation introduces the 'MaDAM Pilot data management infrastructure for biomedical researchers at University of Manchester' project (funded under the Infrastructure strand of the JISC Managing Research Data Programme between October 2009 and June 2010) which aims at producing a technical & governance solution based on researchers’ requirements with flexibility to meet needs across multiple research groups/disciplines and taking into account the institutional landscape and its policies. This encompasses data capture, data storage and data curation, and is designed to add value both to the full lifecycle of research projects and also by making data readily available for reuse.
The pilot is focusing on a specific domain area (Life and Medical Sciences) as input to a wider strategic activity to address the needs of the whole of the University research community. User engagement in the MaDAM project focuses on an iterative user-driven/bottom-up development process together with collecting non-technical requirements.
Bioinformatics databases: Current Trends and Future PerspectivesUniversity of Malaya
Data is the most powerful resource in any field or subject of study. In Biology, data comes from scientists and their actions, while any institution that makes sense of the data collected, will be in the forefront in their respective research field. In the beginning of any data collection endeavour, it is critical to find proper management techniques to store data and to maximise its utilisation. This presentation reflects upon the current trends and techniques of data modeling, architecture with a highlight on the uses of database, focusing on Bioinformatics examples and case studies. Finally, the future of bioinformatics databases is highlighted to give an overview of the modeling techniques to accommodate the biological data escalation in coming years.
The document discusses the University of Virginia School of Data Science (SDS) and opportunities for collaboration with NASA. It provides an overview of SDS, including its mission to be a leader in responsible data science through interdisciplinary collaboration. It describes SDS's data science framework, research areas, capabilities, and recent growth. Examples of current research projects involving NASA data on environmental monitoring and forest ecosystems are presented. The document promotes further partnership between SDS and NASA on challenges in science, medicine, and other domains.
Supporting Research Data Management at the University of StirlingLisa Haddow
The Digital Curation Centre (DCC) provides support to universities to help them manage research data. This includes tools to assess data needs and risks, plan data management, and develop policies. The DCC can help universities develop data management strategies, provide training to researchers, and pilot tools. Its goal is to build research data management capacity across UK higher education. The DCC is working intensively with 18 universities to increase capabilities in these areas over the next year.
Industrial engineering guide to data scienceRamiz Assaf
This document provides an overview of data science and machine learning for industrial engineering students considering a career transition. It defines data science as the ability to understand, process, extract value from, visualize, and communicate data. The data science lifecycle is presented. The document argues that industrial engineering students are well-positioned to transition due to their background in computer/IT skills, math/statistics knowledge, and domain expertise. Resources for learning data analysis software like Excel, Coursera, edX, Udacity, and Kaggle are recommended. The author provides their LinkedIn profile for additional questions.
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The one-covers-all approach in current metadata standards for scientific data has serious limitations in keeping up with the ever-growing data. This paper reports the findings from a survey to metadata standards in the scientific data domain and argues for the need for a metadata infrastructure. The survey collected 4400+ unique elements from 16 standards and categorized these elements into 9 categories. Findings from the data included that the highest counts of element occurred in the descriptive category and many of them overlapped with DC elements. This pattern also repeated in the elements co-occurred in different standards. A small number of semantically general elements appeared across the largest numbers of standards while the rest of the element co-occurrences formed a long tail with a wide range of specific semantics. The paper discussed implications of the findings in the context of metadata portability and infrastructure and pointed out that large, complex standards and widely varied naming practices are the major hurdles for building a metadata infrastructure.
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Developing Data Services to Support Scientific Data Management (v3)
1. School
of
Information
Studies
Syracuse
University
Developing
Data
Services
to
Support
Scien2fic
Data
Management
Xiamen
University
Library
June
8,
2012
Jian
Qin
School
of
Information
Studies
Syracuse
University
http://eslib.ischool.syr.edu/jqin/
2. School
of
Information
Studies
Syracuse
University
Types
of
data
services
Characteristics
of
data
services
A
quick
introduction
of
scientiJic
data
management
WHAT
ARE
DATA
SERVICES?
6/8/12 02:20 Data services, June 8, 2012
3. School
of
Information
Studies
Syracuse
University
Types
of
data
services
(1)
For whom?
Human
Machine
Infrastructure type of services:
• National
• Institutional
6/8/12 02:20 Data services, June 8, 2012
4. School
of
Information
Studies
Syracuse
University
What
data
services?
(1)
Finding relevant data 83%
Developing data management plans 79%
Finding and using available technology infrastructure and
tools 76%
Developing tools to assist researchers 76%
Archiving and curating relevant data and curating it for
long-term preservation and integration across datasets
Providing curatorial and data Stewardship services
Raising awareness and user training
Source: ARL survey report, 2010
6/8/12 02:20 Data services, June 8, 2012
5. School
of
Information
Studies
Syracuse
University
What
data
services?
(2)
• Submission of data
• Data export
• Data format conversion /transformation
• Access to data (discovering and obtaining data)
• IP protection and management
• Educational offerings
• Technical assistance including data management and manipulation
services
• Access to computing facilities
• Curation
• Archive and preservation tools
• Information
• Print and publication services
• Marketing
• Publicity
• Software development services
Source: Marcial & Hemminger, 2010
Data services, June 8, 2012
6/8/12 02:20
6. School
of
Information
Studies
Syracuse
University
Data
providers,
managers,
and
users
Data
Research
Library
center
center
Who best suits for which services?
Acquiring,
processing
Conversion
/Transforming
Metadata
tagging
Discovering
and
obtaining
Analyzing,
visualization
Archiving,
curating,
preservation
Training,
outreaching
Marketing,
publicizing
Distributing,
publishing
6/8/12 02:20 Data services, June 8, 2012
7. School
of
Information
Studies
Syracuse
University
Characteristics of data services
• Repeatable
• Sustainable
Jinancially
and
technically
• A
community
of
practice
• Institutionalization
• Collaboration
and
coordination
• Conformance
to
regulations
and
laws
6/8/12 02:20 Data services, June 8, 2012
8. School
of
Information
Studies
Syracuse
University
What
are
data?
What
are
some
of
the
major
data
formats?
Why
data
formats?
FUNDAMENTALS
OF
DATA
6/8/12 02:20 Data services, June 8, 2012
9. School
of
Information
Studies
Syracuse
University
What
are
data?
(1)
An
artist’s
conception
(above)
depicts
fundamental
NEON
observatory
instrumentation
and
systems
as
well
as
potential
spatial
organization
of
the
environmental
measurements
made
by
these
instruments
and
systems.
http://www.nsf.gov/pubs/2007/nsf0728/nsf0728_4.pdf
6/8/12 02:20 Data services, June 8, 2012
10. School
of
Information
Studies
Syracuse
University
What
are
data?
(2)
6/8/12 02:20 Data services, June 8, 2012
11. School
of
Information
Studies
Syracuse
University
What
are
data?
(3)
6/8/12 02:20 Data services, June 8, 2012
12. School
of
Information
Studies
Syracuse
University
Medical
and
health
data
Standardization
Compliance
Security
http://www.weforum.org/issues/charter-health-data
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13. School
of
Information
Studies
Syracuse
University
The
mul2-‐dimensions
of
data
Research orientation
Data types
Data formats
Levels of
processing
6/8/12 02:20 Data services, June 8, 2012
14. School
of
Information
Studies
Syracuse
University
Scien2fic
data
formats
Common
data
format
Image
formats
Matrix
formats
Microarray
Jile
formats
Communication
protocols
6/8/12 02:20 Data services, June 8, 2012
15. School
of
Information
Studies
Syracuse
University
Scien2fic
&
medical
data
formats
• Medical
and
Physiological
Data
• Chemical
Formats
Formats
– XYZ
—
XYZ
molecule
geometry
Jile
– BDF
—
BioSemi
data
format
(.xyz)
(.bdf)
– MOL
—
MDL
MOL
format
(.mol)
– EDF
—
European
data
– MOL2
—
Tripos
MOL2
format
(.mol2)
format
(.edf)
– SDF
—
MDL
SDF
format
(.sdf)
• Molecular
Biology
data
Formats
– SMILES
—
SMILES
chemical
format
– PDB
—
Protein
Data
Bank
(.smi)
format
(.pdb)
• Bioinformatics
Formats
– MMCIF
—
MMCIF
3D
molecular
model
format
(.cif)
– GenBank
—
NCBI
GenBank
sequence
format
(.gb,
.gbk)
• Medical
Imaging
– FASTA
—
bioinformatics
sequence
– DICOM
—
DICOM
annotated
format
(.fasta,
.fa,
.fsa,
.mpfa)
medical
images
(.dcm,
.dic)
– NEXUS
—
NEXUS
phylogenetic
data
format
(.nex,
.ndk)
6/8/12 02:20 Data services, June 8, 2012
16. School
of
Information
Studies
Syracuse
University
Summary
• ScientiJic
data
formats
are
closely
tied
to
scientiJic
computing
– Data
structure,
model,
and
attributes
– Self-‐descriptive
with
header/metadata
– API
for
manipulating
the
data
– Interoperability:
conversion
between
different
formats
• No
one-‐format-‐Jits-‐all
standard
• Each
standard
has
one
or
more
tools
for
creating,
editing,
and
annotating
dataset
6/8/12 02:20 Data services, June 8, 2012
17. School
of
Information
Studies
Syracuse
University
What
is
a
dataset?
What
are
some
of
the
metadata
standards
for
describing
datasets?
What
is
data
management?
DATASETS,
METADATA,
AND
DATA
MANAGEMENT
6/8/12 02:20 Data services, June 8, 2012
18. School
of
Information
Studies
Syracuse
University
Dataset
classifica2on
Volume
Large-‐volume
Small-‐volume
6/8/12 02:20 Data services, June 8, 2012
19. School
of
Information
Studies
Syracuse
University
Ecological data example: Instantaneous streamflow by watershed
http://www.hubbardbrook.org/data/dataset.php?id=1
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20. School
of
Information
Studies
Syracuse
University
Diabetes data
and trends—
Country level
estimates:
http://apps.nccd.cdc.gov/
DDT_STRS2/
NationalDiabetesPrevale
nceEstimates.aspx?
mode=PHY ;
Diabetes Data &
Trends home page:
http://apps.nccd.cdc.gov/
ddtstrs/default.aspx
6/8/12 02:20 Data services, June 8, 2012
21. Clinical trials data management:
School
of
Information
Studies
Syracuse
University
http://www.clinicaltrials.gov/ct2/show/NCT00006286?term=TADS
+NIMH&rank=1
6/8/12 02:20 Data services, June 8, 2012
22. School
of
Information
Studies
Syracuse
University
Common
in
the
examples
• Attributes
of
a
dataset
tell
users/managers:
– What
the
dataset
is
about
– How
data
was
collected
– To
which
project
the
data
is
related
– Who
were
responsible
for
data
collection
– Who
you
may
contact
to
obtain
the
data
– What
publications
the
data
have
generated
– ??
6/8/12 02:20 Data services, June 8, 2012
23. School
of
Information
Studies
Syracuse
University
Metadata
standards
in
medical
&
health
sciences
Structure
Semantics
Medical
Bioinfomatics
NCBI Taxonomy
Healthcare
images
NCBO Bioportal
UMLS
MeSH (Medical Subject
GenBank
Headings)
GenBank
HL7
DICOM
GenBank
SNOMED CT (Systematized
Nomenclature of Medicine--
Clinical Terms)
6/8/12 02:20 Data services, June 8, 2012
24. School
of
Information
Studies
Syracuse
University
6/8/12 02:20 Data services, June 8, 2012
25. School
of
Information
Studies
Syracuse
University
Research
data
collec2ons
Size Metadata Management
Standards
Larger,
Multiple, Organized
discipline-‐ comprehensive Institutionalized,
based
Heroic
individual
Smaller,
None or inside the
team-‐based
random team
6/8/12 02:20 Data services, June 8, 2012
26. School
of
Information
Studies
Syracuse
University
Datasets,
data
collec2ons,
and
data
repositories
System for storing,
managing,
preserving, and
• Data
collections
are
built
for
providing access to
larger
segments
of
science
datasets
and
engineering
Data
• Datasets
repository
– typically
centered
around
an
A repository may
event
or
a
study
contain one or more
– contain
a
single
Jile
or
multiple
data collections
Jiles
in
various
formats
A data collection may
– coupled
with
documentation
contain one or more
about
the
background
of
data
datasets
collection
and
processing
A dataset may
contain one or more
6/8/12 02:20 Data services, June 8, 2012 data files
27. School
of
Information
Studies
Syracuse
University
6/8/12 02:20 Data services, June 8, 2012
28. School
of
Information
Studies
Syracuse
University
Planning
tool:
SWOT
analysis
6/8/12 02:20 Data services, June 8, 2012
29. School
of
Information
Studies
Syracuse
University
Example
of
SWOT
analysis:
ARL/DLF’s
E-‐Science
Ins2tute
• Baseline
module:
develop
a
basic
understanding
of
eResearch
• Context
module:
environment
scan
• Building
blocks
module:
SWOT
analysis
of
organizational
change,
collaboration,
sustainability,
policy
This example is based on Gail Steinhart’s guest lecture to the
Data Services course at Syracuse iSchool, February, 2012.
6/8/12 02:20 Data services, June 8, 2012
30. School
of
Information
Studies
Syracuse
University
ARL/DLF’s
E-‐Science
Ins2tute:
Baseline
module
• Self-‐assessment:
– Organization
of
research,
IT,
cyberinfrastructure
at
your
institution
– Major
sources
of
funding,
research
areas
– Key
research
centers,
individuals,
partnerships
• Identifying
interview
candidates:
university
administration,
IT
leaders,
key
faculty/researchers
6/8/12 02:20 Data services, June 8, 2012
31. School
of
Information
Studies
Syracuse
University
ARL/DLF’s
E-‐Science
Ins2tute:
Context
module
• Self-‐assessment:
– Institutional
culture
&
priorities
– Current
infrastructure
and
stafJing
– Library
&
change
– Sustainability
and
assessment
of
initiatives
– Other
activities
6/8/12 02:20 Data services, June 8, 2012
32. School
of
Information
Studies
Syracuse
University
ARL/DLF’s
E-‐Science
Ins2tute:
Building
blocks
module
• Turn
interview
transcripts
and
self-‐assessments
into
“statements”
• Classify
statements
as
Strength,
Weakness,
Opportunity,
Threat
6/8/12 02:20 Data services, June 8, 2012
33. School
of
Information
Studies
Syracuse
University
https://confluence.cornell.edu/
display/rdmsgweb/services
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35. School
of
Information
Studies
Syracuse
University
Case
Study
#1:
To
build
or
not
to
build
a
data
repository?
by the researchers in this institution.
A university library has developed an institutional repository for preserving and
providing access to the scholarly output
Now the new challenge arises from e-science research demanding data
management plan by the funding agency and the linking between publications
and data by the authors and users. You already know that some faculty use
their disciplinary data repository for submitting their datasets (e.g., GenBank for
microbiology research data). The problem you face now is whether an
institutional data repository should be built for those who do “small science” and
don’t have funding nor expertise to manage their data.
Questions to be addressed:
• What are the strategies you will use to approach the problem?
• What are the possible solutions for the problem?
• What are some of the tradeoffs for the solutions you will adopt?
6/8/12 02:20 Data services, June 8, 2012
36. School
of
Information
Studies
Syracuse
University
Case
study
#2:
Developing
a
data
taxonomy
The concept of research data management is a stranger to many faculty as
well as your library staff. What is data? What is a data set? These seemingly
simple terms can be very confusing and have different interpretations in
different contexts and disciplines. As part of the data management strategies,
you decide to develop an authoritative data taxonomy for the campus research
community. This data taxonomy will benefit the creation and use of institutional
data policies, data repository or repositories, and data management plans
required of funding agencies.
Questions to be addressed:
• What should the data taxonomy include?
• What form should it take, a database-driven website or a static HTML page?
• Who should be the constituencies in this process?
• Who will be the maintainer once the taxonomy is released?
6/8/12 02:20 Data services, June 8, 2012
37. School
of
Information
Studies
Syracuse
University
Case
study
#3:
Developing
a
data
policy
Data policies play an important role in governing how the data will be managed,
shared, and accessed. It is also an instrument that will fend off potential legal
problems. Data policies have several types: data access and use, data
publishing, and data management. Your university’s Office of Sponsored
Research has some existing policy on data, but it is neither systematic nor
complete. Many of the terms were defined years ago and did not cover the new
areas such as the embargo period of data. As the university has decided to
build a data repository for managing and preserving datasets, a data policy has
become one of the top priorities for both the institution and the data repository.
Questions to be addressed:
• What should the data policy include?
• Who should be the constituencies in this process?
• Who will be the interpretation authority for the data policy?
6/8/12 02:20 Data services, June 8, 2012
38. School
of
Information
Studies
Syracuse
University
Case
study
#4:
Cataloging
datasets
Describing datasets is the process of creating metadata for datasets. In
scientific disciplines, several metadata standards have been developed, e.g.,
the Content Standard for Digital Geospatial Metadata (CSDGM), Darwin Core,
and Ecological Metadata Language (EML). Each of these metadata standards
contains hundreds of elements and requires both metadata and subject
knowledge training in order to use them. Besides, creating one record using
any of these standards will require a tremendous time investment. But you
library does not have such specialized personnel nor have the fund to hire new
persons for the job. The existing staff has some general metadata skills such as
Dublin Core. In deciding the metadata schema for your data repository, you
need to address these questions:
• Should I adopt a metadata standard or develop one tailored to our need?
• How can I learn what metadata elements are critical to dataset submitters and
searchers?
• What are some of the benefits and disadvantages for adopting a standard or
developing a local schema?
6/8/12 02:20 Data services, June 8, 2012
39. School
of
Information
Studies
Syracuse
University
Case
study
#5:
Evalua2ng
data
repository
tools
Research data as a driving force for e-science is inherently a tool-intensive
field. Tools related to data management can be divided into two broad
categories: those for creating metadata records and those for data repository
management. An academic institution decided to build their own data repository
as part of the supporting service for researchers to meet the data management
plan requirement of funding agencies. This data repository development task
was handed down to the library. You the library director have to decide whether
to develop an in-house system or use an off-the-shelf software system. As
usual, you put together a taskforce to find a solution to this challenge. The
questions to be addressed by the taskforce include:
• What are the options available to us?
• What evaluation criteria are the most important to our goal?
• What are the limitations for us to adopt one option or the other?
• How will this option be interoperate with existing institutional repository
system? Or, can the existing repository system used for data repository
purposes?
6/8/12 02:20 Data services, June 8, 2012
40. School
of
Information
Studies
Syracuse
University
References
Readings
• Soehner,
C.,
Steeves,
C.,
&
Ward,
J.
(2010).
E-‐Science
and
data
support
services:
A
study
of
ARL
member
institutions.
http://www.arl.org/bm~doc/escience_report2010.pdf
• Marcial,
L.
H.
&
Hemminger,
B.
M.
(2010).
ScientiJic
Data
Repositories
on
the
Web:
An
Initial
Survey.
Journal
of
the
American
Society
for
Information
Science
and
Technology,
61(10):
2029-‐2048.
• McCormick,
T.
(2009).
A
Web
services
taxonomy:
not
all
about
the
data.
http://tjm.org/public/Web-‐Services-‐Taxonomy_McCormick_v1.1.pdf
• Venugopal,
S.,
Buyya,
R.,
&
Ramamohanarao,
K.
(2006).
A
taxonomy
of
data
grids
for
distributed
data
sharing,
management,
and
processing.
ACM
Computing
Surveys,
38(1):
http://arxiv.org/pdf/cs.DC/0506034
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