Research, researchers, and research data management. Session 1.2 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
A talk delivered by Sally Rumsey, Sarah Barkla and David Tomkins at the Anybook Oxford Libraries Conference 2015 - Adapting for the Future: Developing Our Professions and Services, 21st July 2015
The Challenges of Making Data Travel, by Sabina LeonelliLEARN Project
1st LEARN Workshop. Embedding Research Data as part of the research cycle. 29 Jan 2016. Presentation by Sabina Leonelli, Exeter Centre for the Study of Life Sciences (Egenis) & Department of Sociology, Philosophy and Anthropology, University of Exeter
RDMRose 1.5 Data management and sharing plansRDMRose
Data management and sharing plans. Session 1.5 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
A talk delivered by Sally Rumsey, Sarah Barkla and David Tomkins at the Anybook Oxford Libraries Conference 2015 - Adapting for the Future: Developing Our Professions and Services, 21st July 2015
The Challenges of Making Data Travel, by Sabina LeonelliLEARN Project
1st LEARN Workshop. Embedding Research Data as part of the research cycle. 29 Jan 2016. Presentation by Sabina Leonelli, Exeter Centre for the Study of Life Sciences (Egenis) & Department of Sociology, Philosophy and Anthropology, University of Exeter
RDMRose 1.5 Data management and sharing plansRDMRose
Data management and sharing plans. Session 1.5 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Keynote talk to LEARN (LERU/H2020 project) for research data management. Emphasizes that problems are cultural not technical. Promotes modern approaches such as Git / continuousIntegration, announces DAT. Asserts that the Right to Read in the Right to Mine. Calls for widespread development of contentmining (TDM)
From Open Data to Open Science, by Geoffrey BoultonLEARN Project
1st LEARN Workshop. Embedding Research Data as part of the research cycle. 29 Jan 2016. Presentation by Geoffrey Boulton, University of Edinburgh & CODATA
Presented by Peter Burnhill, Director of EDINA, at PARSE.insight workshop on Preservation, Access and Re-use of Scientific Data, Darmstadt, Germany, 22 September 2009.
Research Data Management in the Humanities and Social SciencesCelia Emmelhainz
This two-part presentation for librarians reviews basic concepts and concerns with research data management, and is targeted to those working with humanists and social scientists. You are free to re-use and modify with attribution.
How can we ensure research data is re-usable? The role of Publishers in Resea...LEARN Project
How can we ensure research data is re-usable? The role of Publishers in Research Data Management, by Catriona MacCallum. 2nd LEARN Workshop, Vienna, 6th April 2016
Introduction to data and text mining - Jisc Digifest 2016Jisc
Text and data mining (TDM) techniques can be applied to a wide range of materials, from published research papers, books and theses, to cultural heritage materials, digitised collections, administrative and management reports and documentation, etc. Use cases include academic research, resource discovery and business intelligence.
This workshop will show the value and benefits of TDM techniques and demonstrate how ContentMine aims to liberate 100,000,000 facts from the scientific literature, and ContentMine will provide a hands on demo on a topical and accessible scientific/medical subject.
Slides for presentation given at the first Digital Humanities Congress held in Sheffield from 6 – 8 September 2012 with the support of the Network of Expert Centres and Centernet.
URL http://www.shef.ac.uk/hri/dhc2012
This presentation was provided by Jan Fransen of the University of Minnesota - Twin Cities during the NISO virtual conference, Research Information Systems: The Connections Enabling Collaboration, held on August 16, 2017.
OU Library Research Support webinar: Data sharingDaniel Crane
Slides from a webinar delivered on 06th February 2018 for OU research staff and students. Covers data sharing policies; Benefits of data sharing; Data repositories; Preparing data for sharing; and Re-using data.
The basics of Research Data Management. Session 1.1 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Keynote talk to LEARN (LERU/H2020 project) for research data management. Emphasizes that problems are cultural not technical. Promotes modern approaches such as Git / continuousIntegration, announces DAT. Asserts that the Right to Read in the Right to Mine. Calls for widespread development of contentmining (TDM)
From Open Data to Open Science, by Geoffrey BoultonLEARN Project
1st LEARN Workshop. Embedding Research Data as part of the research cycle. 29 Jan 2016. Presentation by Geoffrey Boulton, University of Edinburgh & CODATA
Presented by Peter Burnhill, Director of EDINA, at PARSE.insight workshop on Preservation, Access and Re-use of Scientific Data, Darmstadt, Germany, 22 September 2009.
Research Data Management in the Humanities and Social SciencesCelia Emmelhainz
This two-part presentation for librarians reviews basic concepts and concerns with research data management, and is targeted to those working with humanists and social scientists. You are free to re-use and modify with attribution.
How can we ensure research data is re-usable? The role of Publishers in Resea...LEARN Project
How can we ensure research data is re-usable? The role of Publishers in Research Data Management, by Catriona MacCallum. 2nd LEARN Workshop, Vienna, 6th April 2016
Introduction to data and text mining - Jisc Digifest 2016Jisc
Text and data mining (TDM) techniques can be applied to a wide range of materials, from published research papers, books and theses, to cultural heritage materials, digitised collections, administrative and management reports and documentation, etc. Use cases include academic research, resource discovery and business intelligence.
This workshop will show the value and benefits of TDM techniques and demonstrate how ContentMine aims to liberate 100,000,000 facts from the scientific literature, and ContentMine will provide a hands on demo on a topical and accessible scientific/medical subject.
Slides for presentation given at the first Digital Humanities Congress held in Sheffield from 6 – 8 September 2012 with the support of the Network of Expert Centres and Centernet.
URL http://www.shef.ac.uk/hri/dhc2012
This presentation was provided by Jan Fransen of the University of Minnesota - Twin Cities during the NISO virtual conference, Research Information Systems: The Connections Enabling Collaboration, held on August 16, 2017.
OU Library Research Support webinar: Data sharingDaniel Crane
Slides from a webinar delivered on 06th February 2018 for OU research staff and students. Covers data sharing policies; Benefits of data sharing; Data repositories; Preparing data for sharing; and Re-using data.
The basics of Research Data Management. Session 1.1 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Immersive informatics - research data management at Pitt iSchool and Carnegie...Keith Webster
A joint presentation by Liz Lyon and Keith Webster on providing education for librarians engaged in research data management. This was delivered at Library Research Seminar VI, at the University of Illinois Urbana Champaign in September 2014. The presentation looks at a class delivered by Lyon at the University of Pittsburgh's iSchool in 2014, and the related needs for immersive training opportunities amongst experienced practicing librarians, using Carnegie Mellon University's library, led by Webster, as a case study.
The academic research data lifecycle. Session 1.4 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Data Asset Framework (DAF) surveys. Session 3.1 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
The iterative engagement between curation and evaluation in an open research ...ROER4D
The iterative engagement between curation and evaluation in an open research project: A utilization-focused approach Presentation for the AVU Conference 1-3 July 2015 by Sarah Goodier
The iterative engagement between curation and evaluation in an open research ...SarahG_SS
Presentation at the African Virtual University (AVU) in Nairobi, Kenya in July 2015. This practice-based presentation outlines the iterative engagement between ROER4D’s curation strategy and evaluation of this project objective, and analyses how this facilitates development of the evaluation plan. Opportunities and challenges of developing and evaluating a curation strategy for such a large-scale open research project are also highlighted.
RDMRose 2.3 Institutional data repository policiesRDMRose
Policies for institutional research data repositories. Session 2.3 of the RDMRose 2.3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Overview of the UKRDDS pilot project at Univwersity of Edinburgh employing PhD interns to validate metadata about research data created by University of Edinburgh researchers and held in local RDM services solutions. This was presented at IASSIST in June 2016, Bergen, Norway.
In order to be reused, research data must be discoverable.
The EPSRC Research Data Expectations* requires research organisations to maintain a data catalogue to record metadata about research data generated by EPSRC-funded research projects.
Universities are increasingly making research data assets available through repositories or other data portals.
The requirement for a UK research data discovery service has grown as universities become more involved in RDM and capacity develops.
Advocacy in Research Data Management. Session 3.2 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Interviewing a researcher on Research Data Management .Session 2.6 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Big Data Europe: SC6 Workshop 3: The European Research Data Landscape: Opport...BigData_Europe
Slides of the keynote at the 3rd Big Data Europe SC6 Workshop co-located at SEMANTiCS2018 in Amsterdam (NL) on: The European Research Data Landscape: Opportunities for CESSDA by Peter Doorn, Director DANS, Chair, Science Europe W.G. on Research Data. Chair, CESSDA ERIC General Assembly
Introduction to the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
These role cards accompany the slides for session 3.2 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
RDMRose 4.1 Handout institutional case studyRDMRose
This handout accompanies the slides of session 4.1 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Introduction to the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Institutional research data services in Higher Education. Session 1.6 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Institutional research data services in Higher Education. Session 2.1 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Practical research data management. Session 2.2 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Designing library webpages on Research Data Management. Session 2.4 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Metadata and data citation. Session 2.5 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Training researchers in Research Data Management. Session 3.3 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Rdm rose v3-slides-4.1-an-institutional-case-studyRDMRose
An institutional case study of Research Data Management. Session 4.1 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Research Data Management as a wicked problem. Session 4.2 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Review of the workshops. Session 4.3 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Resources for further study on research data management. Session 4.4 of the RDMRose v3 materials.
The JISC funded RDMRose project (June 2012-May 2013) was a collaboration between the libraries of the University of Leeds, Sheffield and York, with the Information School at Sheffield to provide an Open Educational Resource for information professionals on Research Data Management. The materials were revised between November 2014 and February 2015 for the consortium of North West Academic Libraries (NoWAL).
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
1. Research and researchers
May-15
Research Data Management
Workshop 1.2
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose
2. Session 1.2 overview
• Research in Higher Education
• Being a researcher
• Incentives for RDM
• Data curation profiles
• Data asset framework
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose
3. RESEARCH IN HIGHER EDUCATION
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose
4. Research in HEIs
• Research is important to universities!
• There are many stakeholders
– Senior researchers/research groups
– Early career researchers
– Postgraduate Research Students
– Departmental administrators
– Data specialists
• Research funding is often project based
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose
5. Types of academic discipline
(Biglan, 1973)
• Academic disciplines
are different; one
classic taxonomy is
based on the following
factors:
– Hard (paradigmatic) –
soft (non-paradigmatic)
– Pure – applied
– Living – non-living
Hard and pure
Natural
sciences and
mathematics
Hard and
applied
Science-based
professions
Soft and pure
Humanities
and social
sciences
Soft and
applied
Social
professions
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose
Hard knowledge
Soft knowledge
Pureknowledge
Appliedknowledge
6. Academic tribes
(Becher &Trowler, 2001)
• Academic disciplines could be seen as “global
tribes”
• They share:
– A sense of identity and personal commitment
– Myths
– A sense of what is a “contribution”
– Social networks, with gatekeepers
– Formal communication channels: journals and
conferences
• Peer review
– An “invisible college” and informal networks
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose
7. Disciplinary differences
• “Disciplines differ in the ways they structure
themselves, establish identities, maintain
boundaries, regulate and reward practitioners,
manage consensus and dissent, and
communicate internally and externally.” (Klein,
1996, p. 55)
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose
8. “Blurring, cracking and crossing”
(Klein, 1993)
• Disciplines change
• Disciplines overlap
• Most disciplines are a mix of hard/soft; pure/applied
• Proliferation of specialities
– 1000 maths journals with 4500 subtopics (Becher &
Trowler, 2001, p. 14)
– “Research tracks and specialties grow, split, join, adapt
and die” (Klein, 1996, p. 55)
• Theories and methods may be greater common ground
than subject
• Interdisciplinarity as a major creative strategy
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose
9. Example: Geography
• Has fundamental internal divisions: Physical
and human
• Are diverse internationally: different origins: in
Germany, earth science, in France history
• Has seen many new specialties: “human,
cultural, economic, political, urban, and
regional geography as well as biogeography,
geomorphology, climatology, environmental
science and cartography” (Klein, 1996, p. 41)
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose
10. Specialisation, fragmentation,
hybridisation, fluidity
• Have material impact on LIS collection
– The way that journals change titles, are
superceded
– “Scatter” (Palmer, 2010) creates much of the work
for LIS in facilitating access to the vastly complex
body of academic knowledge
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose
11. Activity: Analysing an academic
department
• How would you characterise the subject as a
whole?
• Can you identify some specialities? Do you
know of any very new specialities?
• Identify some examples of interdisciplinarity
or links between this Department and others.
• Share your thoughts with a colleague who
works to support a different discipline
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose
12. BEING A RESEARCHER
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose
13. Video about the daily life of an
academic researcher
• Watch the following two videos:
– http://www.youtube.com/watch?v=sQ_ZzP7g7TQ
– http://www.youtube.com/watch?v=Nc-
5VXdNGkw
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose
14. Video about the daily life of an
academic researcher
• What does the video tell us about the
academic (in particular, scientific) community?
• What does the video tell us about the
personal motives of researchers?
• What is the concept of research and research
data that comes to the fore in this video?
• What other key points did you pick up from
the video?
May-15
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmrose
16. Incentive 1: Direct benefits to
researchers
• Improve the quality of research data
• Provide access to reliable working data
• Allow conclusions to be validated externally
• Apply good record-keeping standards to data capture including in lab and field
electronic notebooks, which enables scientists to draw conclusions from reliable
and trustworthy working research data
• Enable large amounts of data to be analysed and developed across different
locations by maintaining consistency in working practices and interpretations
• Manage relationships between different versions of dynamic or evolving datasets,
and facilitates linkage with other related research and between primary, secondary
and tertiary data
• Ensure valuable knowledge and data originating from short-term research projects
does not become obsolete or inaccessible when funding expires
• Allow data sets to be combined in new and innovative ways
Learning material produced by RDMRose
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17. Incentive 2: ‘Public good’ obligations
• Demonstrate Return on Investment
• Open Access
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18. Incentive 3: Compliance reasons
• Compliance with funding body requirements
• Legal requirements
• Publishers’ requirements
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19. Some issues for researchers
• The nature of data
• How important is it
relative to doing the
research; projects only
get short term funding
• Is infrastructure
available?
• Lack of RDM knowledge
and skills
• No checking of
compliance
• Legal, ethical and
commercial motives
• Desire to keep control
over data
• Informal sharing practices
already exist
• Lack of reuse culture
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20. Good research practice
Open access
Other priorities
Nature of data
Lack of RDM knowledge
& skills
Legal, ethical & commercial
exceptions
Good
Research Data
Management
practices
Academic culture & lack
of reuse culture
Force field analysis of RDM
May-15
Data preservation
Data storage and security
Compliance
The strengths of these forces differ in different contexts
22. Data Curation Profiles
• http://datacurationprofiles.org/
• http://docs.lib.purdue.edu/dcp/
• http://datalib.edina.ac.uk/mantra/libtraining/
CurationProfiles/DCP-interview-PPLS-
Donnelly.pdf
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May-15
23. DATA ASSET FRAMEWORK
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24. Research data interviews
• Witt and Carlson (2007) offer an overview of the research
data interview:
1. What is the story of the data?
2. What form and format are the data in?
3. What is the expected lifespan of the dataset?
4. How could the data be used, reused, and repurposed?
5. How large is the dataset, and what is its rate of growth?
6. Who are the potential audiences for the data?
7. Who owns the data?
8. Does the dataset include any sensitive information?
9. What publications or discoveries have resulted from the data?
10. How should the data be made accessible?
May-15
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25. Data Asset Framework (DAF)
• “A framework developed by the JISC-funded DAFD
project to identify data assets held within Higher
and Further Educational institutions and to
explore how they are managed. The framework is
structured around audit at departmental or unit
level with results being amassed to obtain an
institutional or national perspective.” (Jones,
Ross, & Ruusalepp, 2009, p. 6)
• http://www.data-audit.eu/documents.html
• http://www.data-
audit.eu/docs/DAF_Implementation_Guide.pdf
May-15
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26. Four stages of DAF
• Stage 1 Plan the audit
– Appoint an auditor
– Establish a business case
– Conduct initial research
– Set up audit
• Stage 2 Identify and classify data assets
– Analysis of documentary sources
– Conduct a written survey
– Interviews
– Prepare the data asset inventory
• Vital, important and minor
– Approve and finalise the asset classification
May-15
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27. DAF 4 stages
• Stage 3 Assess the management of data assets
– Collect data on each data asset (Audit form 3: Jones,
Ross, & Ruusalepp, 2009, pp. 45-51)
• Description, provenance, ownership, location, retention,
management
• Stage 4 Reporting results and making
recommendations
– Produce audit report
• Brief overview of the organisation
• Profile of data holdings
• Recommendations for improved asset management
– Meet with management and finalise report
May-15
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28. A data asset record
• Examine the example from the DAF
methodology of a completed extended audit
form 3 for a data asset (Jones, Ross, &
Ruusalepp, 2009, pp.49-51).
• How often is the asset updated?
• How is this asset backed up?
• What file format is it in?
• Did you find the form very “technical”?
May-15
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29. University of Hertfordshire data asset
survey results
• Another useful resource to explore is
http://research-data-
toolkit.herts.ac.uk/2012/08/data-asset-
survey-results/
• It illustrates the range of formats, scale of data
etc. being held by researchers in one
institution
May-15
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31. References
• Becher, T., & Trowler, P.R. (2001). Academic Tribes and
Territories: Intellectual Enquiry and the Culture of
Disciplines (2nd ed.). Philadelphia; Buckingham: Society for
Research into Higher Education; Open University Press.
• Biglan, A. (1973). The characteristics of subject matter in
different academic areas. Journal of Applied
Psychology, 57(3), 195-203.
• Jones, S., Ross, S., & Ruusalepp, R. (2009). Data Audit
Framework Methodology (draft for discussion, version 1.8).
Glasgow: HATII. Retrieved from http://www.data-
audit.eu/DAF_Methodology.pdf.
Learning material produced by RDMRose
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32. References
• Klein, J. T. (1993). Blurring, cracking, and crossing: permeation and
the fracturing of discipline. In E. Messer-Davidow, D. R. Shumway, &
D. Sylvan (Eds.), Knowledges: Historical and Critical Studies in
Disciplinarity (pp. 185-211). Charlottesville: University Press of
Virginia.
• Klein, J. T. (1996). Crossing Boundaries: Knowledge, Disciplinarities,
and Interdisciplinarities. Charlottesville: University Press of Virginia.
• Palmer, C. L. (2010). Information research on interdisciplinarity. In R.
Frodeman, J. T. Klein, & C. Mitcham (Eds.), The Oxford Handbook of
Interdisciplinarity. Oxford; New York: Oxford University Press.
• Witt, M., & Carlson, J. R. (2007). Conducting a Data
Interview. Scientist. West Lafayette, Indiana: Purdue University
Libraries. Retrieved from
http://docs.lib.purdue.edu/lib_research/81/.
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May-15
Editor's Notes
So for any institution or even any academic dept/specialism you might map out the drivers and barriers.