The document outlines an eScience librarianship curriculum that aims to educate librarians for managing research data in the digital era. The curriculum covers key areas like scientific data literacy, data management competencies, and skills for collaborating in eScience initiatives. It consists of core courses in scientific data management and cyberinfrastructure technologies, as well as capstone courses focused on developing the ability to plan and lead eScience librarianship projects. The goal is to produce librarians with expertise in all aspects of the data lifecycle and the ability to support researchers throughout the eScience process.
This document discusses several topics that will drive the future of digital libraries, including data management plans, data citation, curation service models, sustainability, training data practitioners, and more. Specific issues covered include scientific data support, data identifiers, curation best practices, cost models, educating librarians in data management, and the role of digital libraries in enabling reproducible science through 2050.
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.
Functional and Architectural Requirements for Metadata: Supporting Discovery...Jian Qin
The tremendous growth in digital data has led to an increase in metadata initiatives for different types of scientific data, as evident in Ball’s survey (2009). Although individual communities have specific needs, there are shared goals that need to be recognized if systems are to effectively support data sharing within and across all domains. This paper considers this need, and explores systems requirements that are essential for metadata supporting the discovery and management of scientific data. The paper begins with an introduction and a review of selected research specific to metadata modeling in the sciences. Next, the paper’s goals are stated, followed by the presentation of valuable systems requirements. The results include a base-model with three chief principles: principle of least effort, infrastructure service, and portability. The principles are intended to support “data user” tasks. Results also include a set of defined user tasks and functions, and applications scenarios.
Presentation to the UM Library Emergent Research SeriesSEAD
SEAD is a 5-year project funded by the NSF to develop cyberinfrastructure for sustainable data preservation and access. It is a partnership between the universities of Michigan, Indiana, and Illinois. SEAD aims to serve researchers in sustainability science who work in small teams and have diverse data needs. It provides active curation tools, collaboration spaces, and interfaces that integrate data, publications, and people. Data can be deposited to university repositories through the SEAD Virtual Archive for long-term preservation and discovery. Lessons show more support is needed to bridge data production and long-term infrastructure. Future plans include expanding the user community and repository options.
Changing the Curation Equation: A Data Lifecycle Approach to Lowering Costs a...SEAD
This document discusses the Sustainable Environment Actionable Data (SEAD) project, which aims to lower the costs and increase the value of data curation through a data lifecycle approach. SEAD provides lightweight data services to support sustainability research, including secure project workspaces, active and social curation tools, and integrated lifecycle support for data from ingest to long-term preservation. By leveraging technologies like Web 2.0 and standards, SEAD simplifies and automates curation processes using metadata captured from data producers and users. This allows curation activities to begin earlier in the data lifecycle and be distributed across researchers and curators.
Preservation, Publishing, and People: A SEAD ViewInna Kouper
The document discusses research objects (ROs) which bundle together primary research results, metadata, software, and other materials. It describes the roles of data creators, curators, and data scientists in working with ROs as they move from initial research to publication and later reuse. The SEAD Virtual Archive (VA) implements a model for ROs that allows them to transition between different states as they move through the research lifecycle from creation to publication and reuse.
Stuart Phinn_Many kinds of infrastructure: resolving and advancing ecosystem ...TERN Australia
This document discusses infrastructure for ecosystem science in Australia. It begins by outlining the multi-disciplinary nature of ecosystem science and challenges in funding infrastructure to support data collection, storage, analysis and sharing across disciplines. It promotes a collaborative approach through the TERN network to establish shared infrastructure and standards. Examples are given of coordinated data collection, processing, storage and analysis projects enabled by TERN. The document argues that infrastructure like TERN improves the efficiency and effectiveness of ecosystem science in Australia.
This document discusses several topics that will drive the future of digital libraries, including data management plans, data citation, curation service models, sustainability, training data practitioners, and more. Specific issues covered include scientific data support, data identifiers, curation best practices, cost models, educating librarians in data management, and the role of digital libraries in enabling reproducible science through 2050.
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.
Functional and Architectural Requirements for Metadata: Supporting Discovery...Jian Qin
The tremendous growth in digital data has led to an increase in metadata initiatives for different types of scientific data, as evident in Ball’s survey (2009). Although individual communities have specific needs, there are shared goals that need to be recognized if systems are to effectively support data sharing within and across all domains. This paper considers this need, and explores systems requirements that are essential for metadata supporting the discovery and management of scientific data. The paper begins with an introduction and a review of selected research specific to metadata modeling in the sciences. Next, the paper’s goals are stated, followed by the presentation of valuable systems requirements. The results include a base-model with three chief principles: principle of least effort, infrastructure service, and portability. The principles are intended to support “data user” tasks. Results also include a set of defined user tasks and functions, and applications scenarios.
Presentation to the UM Library Emergent Research SeriesSEAD
SEAD is a 5-year project funded by the NSF to develop cyberinfrastructure for sustainable data preservation and access. It is a partnership between the universities of Michigan, Indiana, and Illinois. SEAD aims to serve researchers in sustainability science who work in small teams and have diverse data needs. It provides active curation tools, collaboration spaces, and interfaces that integrate data, publications, and people. Data can be deposited to university repositories through the SEAD Virtual Archive for long-term preservation and discovery. Lessons show more support is needed to bridge data production and long-term infrastructure. Future plans include expanding the user community and repository options.
Changing the Curation Equation: A Data Lifecycle Approach to Lowering Costs a...SEAD
This document discusses the Sustainable Environment Actionable Data (SEAD) project, which aims to lower the costs and increase the value of data curation through a data lifecycle approach. SEAD provides lightweight data services to support sustainability research, including secure project workspaces, active and social curation tools, and integrated lifecycle support for data from ingest to long-term preservation. By leveraging technologies like Web 2.0 and standards, SEAD simplifies and automates curation processes using metadata captured from data producers and users. This allows curation activities to begin earlier in the data lifecycle and be distributed across researchers and curators.
Preservation, Publishing, and People: A SEAD ViewInna Kouper
The document discusses research objects (ROs) which bundle together primary research results, metadata, software, and other materials. It describes the roles of data creators, curators, and data scientists in working with ROs as they move from initial research to publication and later reuse. The SEAD Virtual Archive (VA) implements a model for ROs that allows them to transition between different states as they move through the research lifecycle from creation to publication and reuse.
Stuart Phinn_Many kinds of infrastructure: resolving and advancing ecosystem ...TERN Australia
This document discusses infrastructure for ecosystem science in Australia. It begins by outlining the multi-disciplinary nature of ecosystem science and challenges in funding infrastructure to support data collection, storage, analysis and sharing across disciplines. It promotes a collaborative approach through the TERN network to establish shared infrastructure and standards. Examples are given of coordinated data collection, processing, storage and analysis projects enabled by TERN. The document argues that infrastructure like TERN improves the efficiency and effectiveness of ecosystem science in Australia.
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.
DataCite and Campus Data Services
Paul Bracke, Associate Dean for Digital Programs and Information Services, Purdue University
Research libraries are increasingly interested in developing data services for their campuses. There are many perspectives, however, on how to develop services that are responsive to the many needs of scientists; sensitive to the concerns of scientists who are not always accustomed to sharing their data; and that are attractive to campus administrators. This presentation will discuss the development of campus-based data services programs, the centrality of data citation to these efforts, and the ways in which engagement with DataCite can enhance local programs.
Data Sets, Ensemble Cloud Computing, and the University Library:Getting the ...SEAD
This document discusses research data management and the role of university libraries. It describes the SEAD (Sustainable Environment Actionable Data) project, which provides data services like curation, preservation, and a social community network to support research data across its lifecycle. SEAD aims to support interdisciplinary research by allowing researchers to define and manage related collections of data and metadata called Research Objects in a scalable way. The document argues that research organizations are best positioned to provide comprehensive long-term data services that integrate across the entire research process.
Practical and Conceptual Considerations of Research Object PreservationSEAD
This document discusses research object (RO) frameworks for preserving digital research data. It addresses the challenges of research spanning long periods of time and involving complex, heterogeneous data that changes states. The research object framework aims to capture agents, states, relationships, and content to enable automation, reproducibility, and reuse of research. The framework defines three states for research objects - live, curated, and published. Live objects are works in progress, curated objects are packaged for preservation, and published objects are immutable and citable. The framework allows documentation of research processes and outputs to build trust and facilitate reuse.
This document discusses the Sustainable Environment - Actionable Data (SEAD) project. SEAD aims to provide data services to sustainability researchers by developing tools that address challenges like heterogeneous and small datasets. It plans to move data curation upstream, involve domain scientists, and leverage social media and metadata. SEAD will integrate these active curation services into a federated infrastructure to preserve datasets long-term. The project is led by researchers from multiple institutions and funded by the National Science Foundation.
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
The Digital Curation Centre was created to help build skills and capabilities around research data management in UK higher education by providing support and guidance to address challenges that individual institutions cannot tackle alone. The document discusses why managing research data has become important due to factors like large datasets, funder requirements, and the need for open science. It also examines some of the challenges around issues like scale, infrastructure needs, policies, and developing skills and incentives around data management.
ESA14 Workshop on SEAD's Data Services and ToolsSEAD
This document provides an overview of the SEAD (Sustainable Environment and Ecological Development) services and tools for data curation, preservation, and sharing. It outlines the SEAD workshop agenda which demonstrates how to use project spaces to manage research data, metadata, and social features. It also describes how to publish and preserve data, connect with other researchers through profiles and a research network, and find data within a project space. The goal of SEAD is to provide secure, team-controlled spaces to manage research data throughout the data lifecycle and promote sharing and discovery.
SEAD is a NSF DataNet project that aims to provide cyberinfrastructure for long tail data in sustainability science research. It develops tools for active and social curation of data including an Active Curation Repository (ACR) and VIVO profiles. It also creates a Virtual Archive to facilitate long-term access and preservation of datasets across multiple institutional repositories. The presentation provides an overview of SEAD's approach and highlights pilots with the National Center for Earth Surface Dynamics, including ingesting their data collections into the ACR and Virtual Archive and building a social network in VIVO.
Digital Library Federation - DataNets Panel presentation (Nov. 1st, 2011)SEAD
This document summarizes a panel discussion on the NSF funded Datanet partnerships program. It introduces the panelists from various Datanet projects including SEAD, TerraPop, Datanet Federation Consortium, and DataOne. It then provides more detail on the goals and strategies of the SEAD project, which aims to develop tools and services to address the needs of long-tail sustainability research by leveraging social curation and active metadata. SEAD works to move data curation upstream and engage researchers throughout the project using automated metadata and volunteered contributions.
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.
About the Webinar
Presenters will discuss the role of the library in the academic research enterprise and provide an overview of new librarian strategies, tools, and technologies developed to support the lifecycle of scholarly production and data curation. Specific challenges that face research libraries will be described and potential responses will be explored, along with a discussion of the types of skills and services that will be required for librarians to effectively curate research output.
Instructional Data Sets from Q-step Launch Event (Univ of Exeter) 3-20-2014ICPSR
Presentation about using social science data in the classroom and creating (and finding) resources with which to do it. Addresses both substantive courses and research methods/statistics courses.
Supplementary presentation slides from a lecture on digital preservation given at the University of the West of England (UWE) as part of the MSc in Library and Library Management, University of the West of England, Frenchay Campus, Bristol, March 10, 2010
ESI Supplemental Webinar 2 - DataONE presentation slides DuraSpace
This document provides an overview of a webinar on DataONE, a project that aims to provide tools and approaches for supporting the data life cycle. The webinar covered three key challenges in data management: preservation and planning, discovery, and innovation. It discussed how DataONE is working to address these challenges through its coordinated network of member nodes that allow for data preservation, sharing and discovery. The webinar also demonstrated some of DataONE's tools like the DMPTool for data management planning and the Investigator Toolkit for data analysis and visualization.
ESI Supplemental 1 E-research Support SlidesDuraSpace
E-Research Support at
Johns Hopkins University & Purdue University
Supplemental Webinar
Wednesday, October 17, 2012
Presented by Sayeed Choudhurry & James Mullins
Understanding ICPSR - An Orientation and Tours of ICPSR Data Services and Edu...ICPSR
This is ICPSR's core workshop deck designed to introduce, remind, and refresh your knowledge of ICPSR. It contains four "tours" or sub-presentations describing ICPSR's general reason for being, it's social and behavioral research data complete with search strategies, its training, educational, and instructional resources, and its data management and curation services, data repository options, and support resources (content and budget estimates) for those writing grant proposals.
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.
The document describes the Semantic Scout, a framework developed by CNR Semantic Technology Lab for searching, presenting, and analyzing entities from CNR data sources using semantic web, linked open data, natural language processing, and information retrieval techniques. It summarizes the goals and architecture of the Semantic Scout, including how it converts CNR data into ontologies and triples, publishes and links the data, and allows users to search and explore the data through a SPARQL endpoint and other interfaces. The document also provides an example of how the Semantic Scout can be used to identify experts on a topic by searching the integrated CNR data cloud.
Educating a New Breed of Data Scientists for Scientific Data Management Jian Qin
This presentation reports the data science curriculum development and implementation at Syracuse iSchool, which has shaped by the fast changing data-intensive environment not only for science but also for business and research at large.
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.
DataCite and Campus Data Services
Paul Bracke, Associate Dean for Digital Programs and Information Services, Purdue University
Research libraries are increasingly interested in developing data services for their campuses. There are many perspectives, however, on how to develop services that are responsive to the many needs of scientists; sensitive to the concerns of scientists who are not always accustomed to sharing their data; and that are attractive to campus administrators. This presentation will discuss the development of campus-based data services programs, the centrality of data citation to these efforts, and the ways in which engagement with DataCite can enhance local programs.
Data Sets, Ensemble Cloud Computing, and the University Library:Getting the ...SEAD
This document discusses research data management and the role of university libraries. It describes the SEAD (Sustainable Environment Actionable Data) project, which provides data services like curation, preservation, and a social community network to support research data across its lifecycle. SEAD aims to support interdisciplinary research by allowing researchers to define and manage related collections of data and metadata called Research Objects in a scalable way. The document argues that research organizations are best positioned to provide comprehensive long-term data services that integrate across the entire research process.
Practical and Conceptual Considerations of Research Object PreservationSEAD
This document discusses research object (RO) frameworks for preserving digital research data. It addresses the challenges of research spanning long periods of time and involving complex, heterogeneous data that changes states. The research object framework aims to capture agents, states, relationships, and content to enable automation, reproducibility, and reuse of research. The framework defines three states for research objects - live, curated, and published. Live objects are works in progress, curated objects are packaged for preservation, and published objects are immutable and citable. The framework allows documentation of research processes and outputs to build trust and facilitate reuse.
This document discusses the Sustainable Environment - Actionable Data (SEAD) project. SEAD aims to provide data services to sustainability researchers by developing tools that address challenges like heterogeneous and small datasets. It plans to move data curation upstream, involve domain scientists, and leverage social media and metadata. SEAD will integrate these active curation services into a federated infrastructure to preserve datasets long-term. The project is led by researchers from multiple institutions and funded by the National Science Foundation.
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
The Digital Curation Centre was created to help build skills and capabilities around research data management in UK higher education by providing support and guidance to address challenges that individual institutions cannot tackle alone. The document discusses why managing research data has become important due to factors like large datasets, funder requirements, and the need for open science. It also examines some of the challenges around issues like scale, infrastructure needs, policies, and developing skills and incentives around data management.
ESA14 Workshop on SEAD's Data Services and ToolsSEAD
This document provides an overview of the SEAD (Sustainable Environment and Ecological Development) services and tools for data curation, preservation, and sharing. It outlines the SEAD workshop agenda which demonstrates how to use project spaces to manage research data, metadata, and social features. It also describes how to publish and preserve data, connect with other researchers through profiles and a research network, and find data within a project space. The goal of SEAD is to provide secure, team-controlled spaces to manage research data throughout the data lifecycle and promote sharing and discovery.
SEAD is a NSF DataNet project that aims to provide cyberinfrastructure for long tail data in sustainability science research. It develops tools for active and social curation of data including an Active Curation Repository (ACR) and VIVO profiles. It also creates a Virtual Archive to facilitate long-term access and preservation of datasets across multiple institutional repositories. The presentation provides an overview of SEAD's approach and highlights pilots with the National Center for Earth Surface Dynamics, including ingesting their data collections into the ACR and Virtual Archive and building a social network in VIVO.
Digital Library Federation - DataNets Panel presentation (Nov. 1st, 2011)SEAD
This document summarizes a panel discussion on the NSF funded Datanet partnerships program. It introduces the panelists from various Datanet projects including SEAD, TerraPop, Datanet Federation Consortium, and DataOne. It then provides more detail on the goals and strategies of the SEAD project, which aims to develop tools and services to address the needs of long-tail sustainability research by leveraging social curation and active metadata. SEAD works to move data curation upstream and engage researchers throughout the project using automated metadata and volunteered contributions.
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.
About the Webinar
Presenters will discuss the role of the library in the academic research enterprise and provide an overview of new librarian strategies, tools, and technologies developed to support the lifecycle of scholarly production and data curation. Specific challenges that face research libraries will be described and potential responses will be explored, along with a discussion of the types of skills and services that will be required for librarians to effectively curate research output.
Instructional Data Sets from Q-step Launch Event (Univ of Exeter) 3-20-2014ICPSR
Presentation about using social science data in the classroom and creating (and finding) resources with which to do it. Addresses both substantive courses and research methods/statistics courses.
Supplementary presentation slides from a lecture on digital preservation given at the University of the West of England (UWE) as part of the MSc in Library and Library Management, University of the West of England, Frenchay Campus, Bristol, March 10, 2010
ESI Supplemental Webinar 2 - DataONE presentation slides DuraSpace
This document provides an overview of a webinar on DataONE, a project that aims to provide tools and approaches for supporting the data life cycle. The webinar covered three key challenges in data management: preservation and planning, discovery, and innovation. It discussed how DataONE is working to address these challenges through its coordinated network of member nodes that allow for data preservation, sharing and discovery. The webinar also demonstrated some of DataONE's tools like the DMPTool for data management planning and the Investigator Toolkit for data analysis and visualization.
ESI Supplemental 1 E-research Support SlidesDuraSpace
E-Research Support at
Johns Hopkins University & Purdue University
Supplemental Webinar
Wednesday, October 17, 2012
Presented by Sayeed Choudhurry & James Mullins
Understanding ICPSR - An Orientation and Tours of ICPSR Data Services and Edu...ICPSR
This is ICPSR's core workshop deck designed to introduce, remind, and refresh your knowledge of ICPSR. It contains four "tours" or sub-presentations describing ICPSR's general reason for being, it's social and behavioral research data complete with search strategies, its training, educational, and instructional resources, and its data management and curation services, data repository options, and support resources (content and budget estimates) for those writing grant proposals.
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.
The document describes the Semantic Scout, a framework developed by CNR Semantic Technology Lab for searching, presenting, and analyzing entities from CNR data sources using semantic web, linked open data, natural language processing, and information retrieval techniques. It summarizes the goals and architecture of the Semantic Scout, including how it converts CNR data into ontologies and triples, publishes and links the data, and allows users to search and explore the data through a SPARQL endpoint and other interfaces. The document also provides an example of how the Semantic Scout can be used to identify experts on a topic by searching the integrated CNR data cloud.
Educating a New Breed of Data Scientists for Scientific Data Management Jian Qin
This presentation reports the data science curriculum development and implementation at Syracuse iSchool, which has shaped by the fast changing data-intensive environment not only for science but also for business and research at large.
This document discusses research objects as a framework for facilitating the exchange and reuse of digital knowledge. Research objects are defined as semantically rich aggregations of resources that support a research objective. They allow for workflows, data, documents and other resources to be bundled together and shared. The document outlines several motivating projects, challenges in developing research object models and vocabularies, and a vision for how research objects could allow research to be more efficient, effective and ethical through increased reuse of digital knowledge.
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.
Doing Science Properly in the Digital Age: Software Skills for Free-Range Res...Neil Chue Hong
Keynote given at Digital Research 2012, Oxford, on the current challenges and opportunities for changing the way that software development is taught to researchers. Can we get to the point where the "why" of programming is as important as the "how"?
Carmen O'Dell and Barbara Sen JIBS-RLUK event July 2012sherif user group
RDM Rose by Carmen O'Dell and Barbara Sen, (University of Sheffield). Presentation at Demystifying Research Data: don’t be scared be prepared: A joint JIBS/RLUK event, Tuesday 17th July 17th July 2012, Brunei Gallery at SOAS (School of Oriental and African Studies), London.
Analytic Platforms in the Real World with 451Research and Calpont_July 2012Calpont Corporation
Matt Aslett of 451 Research discussed the rise of analytic platforms and their role in enabling exploratory analytics on large datasets. Bob Wilkinson from Calpont then presented on InfiniDB, Calpont's columnar analytic platform that provides scalable and fast performance for complex queries. InfiniDB was shown to accelerate analytics for telecommunications customer experience data and online advertising attribution. The discussion highlighted how InfiniDB supports flexible schemas and a spectrum of analytic approaches to enable exploratory analysis on structured data.
The document discusses the Wf4Ever project, which aims to create a technological infrastructure for preserving and enabling efficient retrieval and reuse of scientific workflows across disciplines. The project will develop complex research objects that account for the static and dynamic nature of workflows. It will also semantically archive workflows and associated materials to allow for advanced search and recommendation. The project aims to support scientific communities in collaboratively sharing, reusing, and evolving workflows. Key challenges include ensuring quality, preservation, sharing/reuse, classification of workflows and associated resources.
ICTD departmental meeting presentation on repository developmentChris Awre
Chris Awre gave an update on the university's digital repository and research data management activities. The repository uses Fedora and Hydra to store and provide access to a variety of digital content. Recent work included developing a data management plan template to help researchers plan for data management. Looking ahead, efforts will focus on upgrading Hydra, improving image and archive management, and integrating repository searches with the library catalog. Q&A followed the presentation.
The document introduces Sean Bechhofer and provides his contact information, including that he is from the University of Manchester, his email address, Twitter handle, and blog. It then lists several publications and projects related to reproducible and open research, including myExperiment and Research Objects, with the goal of facilitating exchange and reuse of digital knowledge. Key challenges discussed are how to move beyond linear paper publications to frameworks that better support reuse of digital assets like workflows and datasets.
The Advanced Distributed Learning (ADL) Initiative works with the US Coast Guard to standardize and modernize training delivery across the Department of Defense and federal government. ADL provides resources like the Learning Technology Lab and Content Object Repository Discovery to promote accessibility, interoperability, reusability and durability of online educational content. It also supports collaboration through communities of practice to provide expert guidance and identify best practices for technology-enabled learning.
keynote for University is Sussex Partner Network day, 21 June 2012. How Oxford Brookes has made use of learner experience research in developing students digital literacies. Also mapping of SLiDA case stuidies to the developmental framework created with Helen Beetham.
This document discusses trends in instructional design through learning objects. It outlines how learning objects can contribute to designing multimedia materials by moving from teacher-centered to learner-centered education, diversifying and flexibilizing educational offerings, promoting open and distance learning, and optimizing resources through shared projects. Learning objects provide cultural and economic advantages by reducing costs through sharing and supporting constructivist approaches by giving students experiences. Key trends include the reusability, scalability, interdisciplinarity, and generativity of learning objects.
University Of Petroleum And Energy Studies is the first Indian University which has implemented SAP.SAP for HE&R has been able to provide UPES with real time access to student data ,seamless integration of data across all business units, a single portal with complete and controlled access to the entire organization's data, information and knowledge resourses.
Data Management for Librarians: An IntroductionGarethKnight
The document provides an introduction to data management for librarians, outlining key concepts such as the research data lifecycle, challenges in managing digital data over time, best practices for organizing, documenting, and storing data, and resources for data management support. Common problems include difficulty locating, accessing, and understanding data in the long run without proper planning and preservation strategies. The role of librarians is to educate researchers on best practices and provide support and training resources.
This document discusses knowledge discovery in databases (KDD) through the LON-CAPA online educational system. [1] It defines KDD and data mining, describing the tasks, methods, and applications of KDD. [2] The goals are to obtain predictive models of students, help students and instructors use resources more effectively, and provide information to increase student learning. [3] It then discusses the KDD process and data mining methods like classification, clustering, and dependency modeling that can be applied to discover knowledge from educational data.
Thinking Aloud: University Enterprise Architecture Themes and HorizonsAlison Pope
This document summarizes a technology roadmap workshop held in January 2011 at Royal Holloway, University of London. It discusses emerging technology trends over the next 1-5 years including mobile computing, electronic books, and gesture-based computing. It also outlines challenges facing universities like shrinking budgets and digital literacy. Finally, it proposes themes for a university enterprise architecture, including content management, information ecologies, and supporting the student experience through technology applications and processes.
CNI Fall 2011 Meeting Presentation Margaret Hedstrom & Robert McDonald (Dec. ...SEAD
SEAD is a new NSF-funded project that aims to provide sustainable data services for sustainability science research. It will integrate existing technologies and tools to address the needs of researchers working on "long tail" sustainability problems. SEAD is in its initial phase of developing prototypes and will not be ready to accept data until after October 2012. It is a collaboration between researchers at the University of Michigan, Indiana University, University of Illinois, and Rensselaer Polytechnic Institute.
David Brokenshire has extensive experience in software engineering and development with a focus on educational technology. He has a Ph.D. in computer science from Massey University and has worked on several projects involving machine learning, causal modeling, and artificial intelligence applied to education. He is currently the co-founder of Liffsoft.com, which develops tools to automatically fix broken web links using information retrieval and machine learning techniques.
Similar to Preparing eScience librarians -- RDAP 2012 (20)
How Portable Are the Metadata Standards for Scientific Data?Jian Qin
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.
The document outlines a presentation on survey methodology and ethical issues given at Wuhan University in summer 2012. It covers topics such as what a survey is, survey design, quality, and ethical considerations. The presentation includes sections on defining a survey, key elements of survey research design like research questions, sampling, and constructs and measurements, and addressing ethical issues in using surveys.
Data repositories -- Xiamen University 2012 06-08Jian Qin
The document discusses data repositories and services. It begins by defining what a data repository is, noting that it is a logical and sometimes physical partitioning of data where multiple databases reside. It then outlines some key aspects of data repositories, including technical features like standards, software, and staffing requirements. The document also discusses functions of repositories like content management, archiving, dissemination and system maintenance. It provides examples of institutional repositories and data repositories, highlighting characteristics of each. Finally, it provides a case study on Dryad, an international repository for data and publications in biosciences.
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.
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.
This document provides guidance on preparing research papers for international journal publication. It discusses the typical structure of a research paper, including the introduction, literature review, methodology, findings, discussion, and conclusion. The literature review is described as a critical synthesis of previous research that helps contextualize the study and identify gaps. An effective methodology with clearly described hypotheses, data collection, sampling, and analysis is also emphasized. The peer review process is covered, noting common criteria like a paper's contribution, appropriate methods, supported conclusions, and clear communication. Overall, preparing quality papers is outlined as a long process requiring patience, honesty, attention to detail, and understanding differences in writing styles across languages.
Linking Scientific Metadata (presented at DC2010)Jian Qin
Linked entity data in metadata records builds a foundation for semantic web. Even though metadata records contain rich entity data, there is no linking between associated entities such as persons, datasets, projects, publications, or organizations. We conducted a small experiment using the dataset collection from the Hubbard Brook Ecosystem Study (HBES), in which we converted the entities and their relationships into RDF triples and linked the URIs contained in RDF triples to the corresponding entities in the Ecological Metadata Language (EML) records. Through the transformation program written in XML Stylesheet Language (XSL), we turned a plain EML record display into an interlinked semantic web of ecological datasets. The experiment suggests a methodological feasibility in incorporating linked entity data into metadata records. The paper also argues for the need of changing the scientific as well as general metadata paradigm.
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1. Preparing
eScience
Librarians
for
Managing
Research
Data
RDAP
2012,
New
Orleans,
LA
Jian
Qin
School
of
InformaCon
Studies
Syracuse
University
2. NoCons
of
eScience
librarianship
ProacCve
training
for
data
literacy
ConsultaCve
Leader
in
services
for
eScience
data
use
and
iniCaCves
management
AcCve
players
and
contributors
of
data
Part
of
team
curaCon
transcending
disciplinary
boundaries
RDAP
2012,
New
Orleans
2
3. EducaCng
the
new
type
of
workforce
• ScienCfic
data
literacy
(SDL)
project
(hNp://sdl.syr.edu),
2007-‐2009
• E-‐Science
Librarianship
Curriculum
project
(eSLib
hNp://eslib.ischool.syr.edu),
2009-‐2012,
in
partnership
with
Cornell
University
Library
RDAP
2012,
New
Orleans
3
4. A
curriculum
for
eScience
librarianship
• Overall
learning
objecCves:
– Ability
to
arCculate
eScience
and
to
plan
and
develop
eScience
librarianship
projects
– Competency
in
scienCfic
data
management
– Competency
in
cyberinfrastructure
technologies
– Ability
to
collaborate,
communicate,
and
lead
in
eScience
librarianship
projects
RDAP
2012,
New
Orleans
4
5. Ability
to
• ArCculate
eScience
process
and
data
lifecycle
arCculate
• IdenCfy
user
needs
and
translate
eScience
and
to
the
needs
into
system
requirements
plan
and
develop
• Make
plans
for
eScience
eScience
librarianship
project
iniCaCon
and
librarianship
implementaCon
• Conduct
research
on
data
related
projects
issues
such
as
insCtuConal
data
policy,
support
services,
and
technology
adopCon
• Write
grant
proposals
for
obtaining
funding
to
support
eScience
librarianship
projects
RDAP
2012,
New
Orleans
5
6. • ArCculate
data
Competency
in
characterisCcs
scien-fic
data
• Analyze
domain
data
sets
management
and
develop
data
models
• Define
metadata
element
sets
• Develop
specialized
metadata
for
data
curaCon,
preservaCon,
and
access
• Create
metadata
records
for
scienCfic
data
sets
RDAP
2012,
New
Orleans
6
7. • Maintain
informaCon
Competency
in
retrieval
interfaces
cyberinfrastruct • Maintain
informaCon
ure
technologies
exchange
networks
• Program,
write
code,
and
manipulate
scripts
• Use
content
management
systems
• IdenCfy
and
model
data/
work
flows
• Assess
research
needs
for
and
performance
of
CI
tools
RDAP
2012,
New
Orleans
7
8. Ability
to
• Develop
partnership
with
collaborate,
internal
and
external
communicate,
and
organizaConal
units
and
lead
in
eScience
collaborators
librarianship
• Communicate
with
projects
administrators
and
researchers
• Engage
researchers
in
data
management
processes
• IniCate
and
lead
in
eScience
librarianship
projects
RDAP
2012,
New
Orleans
8
9. The
curriculum
Courses
Primary
learning
outcomes
in
eScience
librarianship
projects
Ability
to
collaborate,
communicate,
and
lead
ScienCfic
Data
Competency
in
scienCfic
data
Management
(core)
management
Competency
in
Cyberinfrastructure
(core)
cyberinfrastructure
technologies
Ability
to
arCculate
eScience
and
to
Data
services
(capstone)
plan
and
develop
eScience
librarianship
projects
Database
systems
(required
elecCve)
Metadata
(elecCve)
RDAP
2012,
New
Orleans
9
10. Theme
1:
building
fundamentals
1
2
Case
studies
that
use
Overview
of
scienCfic
data
pracCcal
examples
to
guide
management
that
covers
students
step-‐by-‐step
in
data
and
metadata
data
analysis
and
fundamentals
management
3
Using
scienCfic
data,
which
involves
discussions
of
data
quality,
data
repositories
and
discovery,
data
analysis
and
presentaCon,
and
ethics
and
intellectual
property
issues
RDAP
2012,
New
Orleans
10
11. Building
fundamentals:
data
formats
Overview
of
scienti.ic
data
management
that
covers
data
and
metadata
fundamentals
Data
NASA’s
de-inition
of
data
Processing
level
level
processing
levels
Level
4
Self-‐descripCve
informaCon
existed
as
Level
Reconstructed
unprocessed
instrument
0
data
at
full
resolutions.
Level
3
header
of
the
data
file
Level
Reconstructed,
unprocessed
instrument
Level
2
1A
data
at
full
resolution,
time
referenced,
and
annotated
with
ancillary
information,
Common
Data
Format
(CDF)
Level
1B
Flexible
Image
Transport
System
(FITS)
but
not
applied
to
the
Level
0
data.
GRid
In
Binary
(GRIB)
Level
Level
1A
data
that
has
been
processed
to
Level
1A
Hierarchical
Data
Format
(HDF)
1B
sensor
units.
Not
all
Network
Common
Data
Format
(netCDF)
instruments
will
have
a
Level
1B
Level
0
equivalent.
Major
scienCfic
data
format
RDAP
2012,
New
Orleans
11
12. Building
fundamentals:
Understanding
data
and
metadata
Data
formats
Processing
levels
Data
collecCons
Some
formats
contain
self-‐
Lineage
vital
to
descripCve
metadata
assessing
data
Metadata
standards
need
quality
to
be
adjusted
for
local
descripCon
needs
RDAP
2012,
New
Orleans
12
13. Building
fundamentals:
data
literacy
IL:
ACRL.
(2010).
DL:
Finn,
Charles,
W.P.
(Tech
&
Learning,
2004)
SDL:
Qin,
J.
&
J.
D’Ignazio,
(Journal
of
Library
Metadata,
2010)
RDAP
2012,
New
Orleans
13
14. Theme
2:
Analysis
and
generalizaCon
Analysis
of
data
problems
is
an
analysis
of
domain
data,
requirements,
and
workflows
that
will
lead
to
the
development
of
soluCons.
RDAP
2012,
New
Orleans
14
15. Analysis
and
generalizaCon:
engaging
in
real
research
projects
• Engage
students
in
research
and
service
projects
– Data
policy
analysis
– Data
management
consultaCon
– Interviews
and
survey
design
• Course
projects
– Real-‐world
data
management
problems
RDAP
2012,
New
Orleans
15
16. Theme
3:
collaboraCon
and
communicaCon
• Community
of
pracCce
• InsCtuConalizaCon
of
data
services
– Data
policies
– Compliance
to
funding
agency
policies
and
mandates
– Infrastructural
data
services
at
insCtuConal,
community,
and
naConal
levels
• Awareness,
incenCves,
and
training
RDAP
2012,
New
Orleans
16
17. CollaboraCon
and
communicaCon
• Mentoring
by
Cornell
librarians,
led
by
Gail
Steinhart
• Internships
in
academic
libraries
and/or
research
centers
• Guest
speakers
to
classes
• Engaging
students
in
research
and
service
projects
RDAP
2012,
New
Orleans
17
18. Evolving
curriculum
CAS
in
Data
Science
Required
courses:
• Database
• Applied
Data
Science
Data
storage
Data
Data
Systems
and
analyCcs
visualizaCon
management
management
RDAP
2012,
New
Orleans
18