Open science framework – Jeff Spies, Centre for Open Science
Active research from lab to publication – Simon Coles, University of Southampton
Managing active research in the university – Robin Rice, University of Edinburgh
Making research available: FAIR principles and Force 11 - David De Roure, Oxford e-Research Centre
Jisc and CNI conference, 6 July 2016
Data management: The new frontier for librariesLEARN Project
Presentation at 3rd LEARN workshop on Research Data Management, “Make research data management policies work”, by Kathleen Shearer, COAR, CARL/ABCR, RDC/DCR, ARL, SSHRC/CSRH.
Open science framework – Jeff Spies, Centre for Open Science
Active research from lab to publication – Simon Coles, University of Southampton
Managing active research in the university – Robin Rice, University of Edinburgh
Making research available: FAIR principles and Force 11 - David De Roure, Oxford e-Research Centre
Jisc and CNI conference, 6 July 2016
Data management: The new frontier for librariesLEARN Project
Presentation at 3rd LEARN workshop on Research Data Management, “Make research data management policies work”, by Kathleen Shearer, COAR, CARL/ABCR, RDC/DCR, ARL, SSHRC/CSRH.
UK and US positions on open access – Steven Hill, HEFCE and Sarah Thomas, Harvard University
University of California and university digital library costing models – MacKenzie Smith, UC Davis
Total cost of ownership and flipped OA – Liam Earney, Jisc
Jisc and CNI conference, 6 July 2016
Supporting Research Data Management in UK Universities: the Jisc Managing Res...L Molloy
Research data management in the UK: interventions by the Jisc Managing Research Data programme and the Digital Curation Centre. Specifies the importance of academic librarians for RDM. Includes links to openly available training resources. Presentation by L Molloy to ExLibris event, 'Excellence in Academic Knowledge Management', Utrecht, 29 October 2013.
UK Research Data Management: overview to ADBU congress, 19 Sep 2013 by Laura ...L Molloy
Research data management in the UK: interventions by the Jisc Managing Research Data programme and the Digital Curation Centre. Specifies the importance of academic librarians for RDM. Includes links to openly available training resources. Presentation by L Molloy to ABDU congress, 19 Sep 2013 in Le Havre.
Open Data in a Big Data World: easy to say, but hard to do?LEARN Project
Presentation at 3rd LEARN workshop on Research Data Management, “Make research data management policies work”
Helsinki, 28 June 2016, by Sarah Callaghan, STFC Rutherford Appleton Laboratory
RDM and data sharing landscape: overview for Salford DCC training 20140522L Molloy
Research data management and data sharing: a brief overview of where we are in the UK right now and some main drivers and benefits. Prepared for Salford university Digital Curation Centre training session, 22 May 2014. Contains material from across DCC resources.
Meeting the Research Data Management Challenge - Rachel Bruce, Kevin Ashley, ...Jisc
Universities and researchers need to be able to manage research data effectively to fulfil research funders requirements and ultimately to contribute to research excellence. UK universities are comparatively well advanced in what is a global challenge, but none the less there needs to be further advances in university policy, technical and support services. This session will share best practice in research data management and information about key tools that can help to develop university solutions; and it will also inform participants about the latest Jisc initiatives to help build university research data services and shared services.
UK and US positions on open access – Steven Hill, HEFCE and Sarah Thomas, Harvard University
University of California and university digital library costing models – MacKenzie Smith, UC Davis
Total cost of ownership and flipped OA – Liam Earney, Jisc
Jisc and CNI conference, 6 July 2016
Supporting Research Data Management in UK Universities: the Jisc Managing Res...L Molloy
Research data management in the UK: interventions by the Jisc Managing Research Data programme and the Digital Curation Centre. Specifies the importance of academic librarians for RDM. Includes links to openly available training resources. Presentation by L Molloy to ExLibris event, 'Excellence in Academic Knowledge Management', Utrecht, 29 October 2013.
UK Research Data Management: overview to ADBU congress, 19 Sep 2013 by Laura ...L Molloy
Research data management in the UK: interventions by the Jisc Managing Research Data programme and the Digital Curation Centre. Specifies the importance of academic librarians for RDM. Includes links to openly available training resources. Presentation by L Molloy to ABDU congress, 19 Sep 2013 in Le Havre.
Open Data in a Big Data World: easy to say, but hard to do?LEARN Project
Presentation at 3rd LEARN workshop on Research Data Management, “Make research data management policies work”
Helsinki, 28 June 2016, by Sarah Callaghan, STFC Rutherford Appleton Laboratory
RDM and data sharing landscape: overview for Salford DCC training 20140522L Molloy
Research data management and data sharing: a brief overview of where we are in the UK right now and some main drivers and benefits. Prepared for Salford university Digital Curation Centre training session, 22 May 2014. Contains material from across DCC resources.
Meeting the Research Data Management Challenge - Rachel Bruce, Kevin Ashley, ...Jisc
Universities and researchers need to be able to manage research data effectively to fulfil research funders requirements and ultimately to contribute to research excellence. UK universities are comparatively well advanced in what is a global challenge, but none the less there needs to be further advances in university policy, technical and support services. This session will share best practice in research data management and information about key tools that can help to develop university solutions; and it will also inform participants about the latest Jisc initiatives to help build university research data services and shared services.
Stewardship data-guidelines- research information network jan 2008Eldad Sotnick-Yogev
Although dated - January 2008 - this document serves as an excellent introduction to the questions any organisation needs to ask as they bring in a Data Management Platform (DMP). From page 6 the questions they highlight are effective in helping think through the roles, rights, responsibilities and relationships that need to be accounted for
A presentation by Dr Lesley Thompson, Director of Science & Engineering, EPSRC - given at the Open Science Showcase held by the Royal Society of Chemistry on 26 February 2014.
Research Data Management Services at UWA (November 2015)Katina Toufexis
Research Data Management Services at the University of Western Australia (November 2015).
Created by Katina Toufexis of the eResearch Support Unit (University Library).
CC-BY
Responsible research: professionalism and integrity. The practical, legal and...Marlon Domingus
Research is in transition. What are the conflicts of interests for the main stakeholders: Academia, Society, Industry. What is the role of the European Commission? What are the technical and legal issues?
Presented as an honors college at Hanzehogeschool Groningen, January 4 2016.
An overview of the LSHTM Research Data Management Policy, outlining the motivations for its introduction, obligations that need to be met and the support available
Presentation at the La Trobe University Open Access Seminar on 25 October 2013. What are the benefits of exposing research data? Why is La Trobe University doing this? What tools does the Library provide to help with this?
Presented by Ms Diane Quarless, Director, ECLAC subregional headquarters for the Caribbean, at the LEARN Caribbean Research Data Workshop. http://learn-rdm.eu/en/workshops/eclac-mini-workshops/3rd-mini-workshop
Presented by Ms Bernadette Lewis, Secretary General, Caribbean Telecommunications Union at the LEARN Caribbean Research Data Workshop. http://learn-rdm.eu/en/workshops/eclac-mini-workshops/3rd-mini-workshop
Gestion de datos para la investigacion: el caso peruano by Edward Mezones, Su...LEARN Project
Gestion de datos para la investigacion: el caso peruano by Edward Mezones, Superintendencia Nacional de Salud (Perú) - presented at the 4th LEARN RDM Workshop in Santiago, Chile: http://learn-rdm.eu/
TALLER LEARN SOBRE DATOS DE INVESTIGACIÓN IMPLEMENTACIÓN DE POLÍTICAS Y ESTRA...LEARN Project
TALLER LEARN SOBRE DATOS DE INVESTIGACIÓN IMPLEMENTACIÓN DE POLÍTICAS Y ESTRATEGIAS EN AMÉRICA LATINA Y EL CARIBE by Miguel Ángel Márdero Arellano, IBICT (Brazil) - presented at the 4th LEARN RDM Workshop in Santiago, Chile: http://learn-rdm.eu/
Avances en torno a la Ley 26.899 e iniciativa regional de datos primarios de...LEARN Project
Avances en torno a la Ley 26.899 e iniciativa regional de datos primarios de investigación by Alberto Apollaro - presented at the 4th LEARN RDM Workshop in Santiago, Chile: http://learn-rdm.eu/
“Data for Development – the value of data for research and society” by Dr. Ma...LEARN Project
“Data for Development – the value of data for research and society”, Dr. Martin Hilbert, University of California - presented at the 4th LEARN RDM Workshop in Santiago, Chile: http://learn-rdm.eu/
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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).
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
LEARN Final Conference: Tutorial Group | Using the LEARN Model RDM Policy
1. GROUP 4
Using the
LEARN RDM Policy
&
Guidance
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139.
2. Outline
1. About Research Data
2. Understanding Policies From
Taboos to Policies
3. Model Policy for Research Data
Management (RDM) at Research
Institutions/Institutes
4. Guideline, Further Developments
and Outreach
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654139.
Ensuring legal and ethical compliance is key issue in this context
Ensuring legal and ethical compliance is key issue in this context
3. World of data
Raw data (primary data)
Processed Data
Negative Results
Processed Data
Processed Data
Inconclusive
Results
Processed Data
Processed Data
Shared
Data
Processed Data
Positive results
Positive results
Shared
Data
Shared
Data
Pub.
Data
OA
Pub.
DataReleased
Data
Different levels of processing of data
Model for digital archiving
Ensuring legal and ethical compliance is key issue in this context
Strata of research data
Restricted Data
Open data
Published data
Open access published data
4. Mission
• Produce a Policy and a Guidance
which can be tailored by any
University or Research Institution to
meet their needs
• Enhance Policy Coordination &
Alignment
6. Going over to related Principles
Going over to the creation of a Policy
Starting with some Taboos
Looking for an Ontology
From Taboos to Policies
1
2
3
Going over to Rules, Legislations and
Regulations (canons, norms, guidelines)4
7. Taboo
A taboo is something, which is forbidden or
disapproved of, or placed under a social
prohibition.
“Thou shalt not delete scientific data“
“Thou shalt not destroy infrastructures”
Usually a negative assertion.
In society and academic environment taboos are
accepted only if they are just a few.
8. Principle
A principle is a fundamental truth or proposition that serves
as the foundation for a system of belief or behaviour or for
a chain of reasoning.
Research data are to be preserved
Format: positive assertion:
Derivation for an academic institution or an academic
service provider: beliefs governing the organization’s
(body) behaviour.
Research data are to be kept FAIR - Findable, Accessible,
Interoperable, Reusable.
Research data infrastructures are to be kept accessible
9. Policies/ 1
A policy is…
- a course or principle of action adopted or proposed by an
organization (or individual);
“The Institution [name XY] will preserve its research data
infrastructure always accessible and free to its members
according to the FAIR principles”
- a development generated from the bottom (resulting from the
action of individuals);
- a development generated from the top (resulting from the
action of an executive);
N.B.: the original Greek ideal of “the projection of the volition of an individual”
is expressed through the politeia and therefore included in this principle of
action.
10. Policies /2
General assumptions concerning policies:
•A single Policy: the policy is a single entity, it should not be in
competition with other policies
•Policy offers the frame for the generation of Rules
•Policy is usually accepted after a while
•Creators of Policy do not want to modify it
•“Policies lag behind” (usually policies are oriented to the past.
Most Policies are reflections of existing conventions)
•Valid for long periods of time – and there is an end (expiry date)
11. Rules, Regulations/ 1
Rules are prescribing conducts or actions. They are generated
by the founder of “orders”. Characteristics of rules are:
- There may exist “lots of rules”: the number of rules can be
„endless“.
- Rules are not always clear (they often need interpretation
according to the situation).
- Rules are usually accepted, but often imposed procedures.
- It is allowed to modify Rules by definition.
- Rules are only valid during a specified period of time.
- The Law is an expression of rules - Law (usually written order
or direction or legal precept or doctrine)
12. Rules, Regulations /2
Example:
“Our University will maintain accessible our
infrastructure each day from 9:00 a.m. to 12:00
a.m and offer support only on Friday from 7:00
a.m. to 8:00 a.m. The research data, that are
publicly funded are to be kept free and accessible
to all members of our University each Sunday,
from 9:00 to 12:00 a.m.“
13. From Taboos to Policies
Taboos Principles Policies Rules
Negative assertion
few
“You shall not delete
scientific data”
“Youl shall not
destroy
infrastrcutures”
Positive assertion
more than „few“
“Research data are
to be kept FAIR -
Findable,
Accessible,
Interoperable,
Reusable.”
“Research data
infrastructures are
to be kept
accessible”
A course or principle
of action. Policy offers
the frame for the
generation of Rules,
should not be in
competition with
other policies
“The Institution
[name XY] will
preserve its research
data infrastructure
always accessible and
free to its members
according to the FAIR
principles”
Rules prescribe conducts
or actions; define who
what when and where
should be done according
to the Policy
“Our University will
maintain accessible our
infrastructure each day
from 9:00 a.m. to 12:00
a.m and offer support
only on Friday from 7:00
a.m. to 8:00 a.m. “
14. Why these differentiations?
• It is important to identify the different
semantic levels
• Understand the differences between
Taboos, Principles, Policies, Rules and
Regulations
• Understanding of the semantic
hierarchy is useful in order to produce
appropriate guidelines
16. How we continued
• Creation of first model policy and guidance
• Continuous involvement of LEARN Partners
• Discussion of policy insights and results at 5
Partner Workshops in London, Vienna, Helsinki,
Santiago de Chile and Barcelona
• Co-operation in Mini-Workshops in the Latin
America area to compare and standardise
terminology and to foster policy alignment
• 12/2016 – 02/2017: Peer review process of
Model Policy and Guidance
17.
18. 1. Preamble
2. Jurisdiction
3. Intellectual Property Rights
4. Handling Research Data
5. Responsibilities, Rights,
Duties
5.1. Researchers are responsible for:...
5.2. The [name of research institution] is
responsible for:…
6. Validity
19. 1. Preamble
The [name of research institution] recognizes the
fundamental importance of research data
and the management of related
administrative records in maintaining quality
research and scientific integrity, and is
committed to pursuing the highest standards.
The [name of research institution]
acknowledges that correct and easily
retrievable research data are the foundation
of and integral to every research project.
They are necessary for the verification and
defence of research processes and results.
RDM policies are highly valuable to current
and future researchers. Research data have
a long-term value for research and
academia, with the potential for widespread
use in society.
20. 2. Jurisdiction
This policy for the management of research data
applies to all researchers active at the [name
of research institution]. The policy was
approved by the [dean/commission/authority]
on [date]. In cases when research is funded
by a third party, any agreements made with
that party concerning intellectual property
rights, access rights and the storage of
research data take precedence over this
policy.
21. 3. Intellectual Property Rights
Intellectual property rights (IPR) are defined in
the work contract between a researcher and his
or her employer. IPRs might also be defined
through further agreements (e.g. grant or
consortial agreements). In cases where the IPR
belong to the institution that employs the
researcher, the institution has the right to choose
how to publish and share the data.
22. 4. Handling research data (1/2)
Research data should be stored and made available
for use in a suitable repository or archiving
system, such as [name of institutional
repository/archiving system, if applicable]. Data
should be provided with persistent identifiers.
It is important to preserve the integrity of research
data. Research data must be stored in a correct,
complete, unadulterated and reliable manner.
Furthermore, they must be identifiable,
accessible, traceable, interoperable, and
whenever possible, available for subsequent use.
In compliance with intellectual property rights, and if
no third-party rights, legal requirements or
property laws prohibit it, research data should be
assigned a licence for open use.
23. 4. Handling research data (2/2)
Adherence to citation norms and requirements regarding publication and
future research should be assured, sources of subsequently-used
data explicitly traceable, and original sources can be
acknowledged.
Research data and records are to be stored and made available
according to intellectual property laws or the requirements of third-
party funders, within the parameters of applicable legal or
contractual requirements, e.g. EU restrictions on where
identifiable personal data may be stored. Research data of future
historical interest and the administrative records accompanying
research projects should also be archived.
The minimum archive duration for research data and records is 10 years
after either the assignment of a persistent identifier or publication
of a related work following project completion, whichever is later.
In the event that research data and records are to be deleted or
destroyed, either after expiration of the required archive duration
or for legal or ethical reasons, such action will be carried out only
after considering all legal and ethical perspectives. The interests
and contractual stipulations of third-party funders and other
stakeholders, employees and partner participants in particular, as
well as the aspects of confidentiality and security, must be taken
into consideration when decisions about retention and destruction
are made. Any action taken must be documented and be
accessible for possible future audit.
24. 5. Responsibilities, Rights, Duties
This policy for the management of research data
applies to all researchers active at the [name
of research institution]. The policy was
approved by the [dean/commission/authority]
on [date]. In cases when research is funded by
a third party, any agreements made with that
party concerning intellectual property rights,
access rights and the storage of research data
take precedence over this policy.
25. 5.1. Researchers are responsible for:
a)Management of research data and data sets in adherence with principles and
requirements expressed in this policy;
b)Collection, documentation, archiving, access to and storage or proper destruction
of research data and research-related records. This also includes the definition of
protocols and responsibilities within a joint research project. Such information
should be included in a Data Management Plan (DMP), or in protocols that
explicitly define the collection, administration, integrity, confidentiality, storage, use
and publication of data that will be employed. Researchers will produce a DMP for
every research project.
c)Compliance with the general requirements of the funders and the research
institution; special requirements in specific projects should be described in the
DMP;
d)Planning to enable, wherever possible, the continued use of data even after
project completion. This includes defining post-project usage rights, with the
assignation of appropriate licences, as well as the clarification of data storage and
archiving in the case of discontinued involvement at the [name of
university/research institution];
e)Backup and compliance with all organisational, regulatory, institutional and other
contractual and legal requirements, both with regard to research data, as well as
the administration of research records (for example contextual or provenance
information).
f)To ensure appropriate institutional support, it is required that new research
projects are registered at the proposal stage at [name of research institution/central
body].
26. 5.2. The [name of research institution] is
responsible for:
a)Empowerment of organisational units, providing appropriate means
and resources for research support operations, the upkeep of services,
organizational units, infrastructures, and employee education;
b)Support of established scientific practices from the beginning. This is
possible through the drafting and provision of DMPs, monitoring,
training, education and support, while in compliance with regulations,
third-party contracts for research grants, university/institutional statutes,
codes of conduct, and other relevant guidelines;
c)Developing and providing mechanisms and services for the storage,
safekeeping, registration and deposition of research data in support of
current and future access to research data during and after the
completion of research projects;
d)Providing access to services and infrastructures for the storage,
safekeeping and archiving of research data and records, enabling
researchers to exercise their responsibilities (as outlined above) and to
comply with obligations to third-party funders or other legal entities.
27. 6. Validity
This policy will be reviewed and updated as
required by the head of/the director of the [name
the research institution] every [two years].
28. Published in LEARN Toolkit in April
2017
http://learn-rdm.eu/wp-
content/uploads/RDMToolki
t.pdf?pdf=RDMToolkit
30. Guidance Document for
Policy Development Published in LEARN Toolkit:
http://learn-rdm.eu/wp-
content/uploads/RDMToolkit.pdf?pdf
=RDMToolkit
31. Outreach to Continental
Europe: AUSTRIA
• Merge of LEARN findings and
Use Case in Austria
• Adaptation to needs of five
Austrian Art Universities and
(started) four Medical
Universities
• Validation of Policy for
discipline-specific needs
32. Outreach to Continental
Europe: ITALY
• Expansion of policy
activities to Italy (mainly in
Venice, Padua, Milan and
through CINECA)
• Validation of Policy in Italian
language
33. Outreach to LATIN AMERICA
• ECLAC study on RDM policies in LAC
• Mini-Workshops with ECLAC
34. Policy Evaluation Grid
July 2015-August 2016:
Collection and analysis
of over 40 European
RDM policies with the
use of an analysis grid
with 25 criteria
Results available for download at:
http://phaidra.univie.ac.at/o:459219