A short review of the new initiatives related to research data management at Harvard University for the CRADLE workshop at IASSIST 2017 (http://www.iassist2017.org/).
Stop press: should embargo conditions apply to metadata?Jisc RDM
Sarah Middle of Cambridge University discusses whether embargo conditions should apply to metadata. Session held at the Research Data Network event in May 2016, Cardiff University.
The challenge of sharing data well, how publishers can helpVarsha Khodiyar
Researchers, academic institutes and funders are increasingly recognizing the importance of data sharing for reproducible science. However, it is not always straightforward and clear to researchers as to how best to share data in a useful way. At Springer Nature we are working on several initiatives to help facilitate the sharing of research data in a reusable way, with our overarching goal being to publish research that is robust and reproducible. I will talk about the effort that goes into our flagship data journal, Scientific Data, to facilitate best practices in publication and sharing of research data, and share some of our experiences publishing Challenge datasets. I will also describe some of the newer Research Data Services that are now available to help all researchers (not only Springer Nature authors) to share their data in a useful way.
This talk was given by Brianna Marshall and Ryan Schryver at a joint informational session hosted by the College of Letters & Science Pre-Award Services, the College of Agricultural and Life Sciences, the College of Engineering, and Research and Sponsored Programs.
A short review of the new initiatives related to research data management at Harvard University for the CRADLE workshop at IASSIST 2017 (http://www.iassist2017.org/).
Stop press: should embargo conditions apply to metadata?Jisc RDM
Sarah Middle of Cambridge University discusses whether embargo conditions should apply to metadata. Session held at the Research Data Network event in May 2016, Cardiff University.
The challenge of sharing data well, how publishers can helpVarsha Khodiyar
Researchers, academic institutes and funders are increasingly recognizing the importance of data sharing for reproducible science. However, it is not always straightforward and clear to researchers as to how best to share data in a useful way. At Springer Nature we are working on several initiatives to help facilitate the sharing of research data in a reusable way, with our overarching goal being to publish research that is robust and reproducible. I will talk about the effort that goes into our flagship data journal, Scientific Data, to facilitate best practices in publication and sharing of research data, and share some of our experiences publishing Challenge datasets. I will also describe some of the newer Research Data Services that are now available to help all researchers (not only Springer Nature authors) to share their data in a useful way.
This talk was given by Brianna Marshall and Ryan Schryver at a joint informational session hosted by the College of Letters & Science Pre-Award Services, the College of Agricultural and Life Sciences, the College of Engineering, and Research and Sponsored Programs.
Data citation metrics : best practice to enable new metrics for research dataLe_GFII
Intervention de Nigel Robinson, Director, Content Managment chez Thomson Reuters au Forum du GFII 2015 : http://forum.gfii.fr/forum/les-nouvelles-mesures-de-l-influence-scientifique-l-apport-des-metriques-alternatives-au-pilotage-de-la-recherche
A template for a basic data management plan. Handout to accompany the presentations Introduction to Research Data Management and Preparing Your Research Data for the Future.
From Data Policy Towards FAIR Data For All: How standardised data policies ca...Rebecca Grant
There is evidence that good data practice leads to increased citation, increased reproducibility, increased productivity, reduced harm and costs of biased or non-transparent research, and that it helps researchers with career progression and provides a better return on investment in research funding. In this presentation we will share feedback on data sharing from a survey of more than 11,000 researchers globally, as well as evidence from our own implementation of standardised data policies and the work of the Research Data Alliance’s Data Policy Implementation Interest Group.
Update from Data policy standardisation and implementation IGVarsha Khodiyar
Update given to the Research Data Alliance Plenary 12 joint meeting session: WG FAIRSharing Registry and Data Policy Standardisation and Implementation IG, on Monday 5th November 2018, Gaborone, Botswana
Public access to research results at USDACyndy Parr
An update on public access activities at the National Agricultural Library and next steps, presented 11 January 2017 at the Earth Science Information Partners (ESIP) meeting in Bethesda, Maryland.
In early 2014, we asked science and social science researchers...
• What expectations do the terms publication and peer review raise in reference to data?
• What features would be useful to evaluate the trustworthiness, evaluate the impact, and enhance the prestige of a data publication?
Slides presented during the "Open Access Research Data Sharing Requirements: Are you ready?” on 27 Oct 2016 @ NTU.
Questions and comments from the participants have been posted on Scholarly Communication Blog (https://blogs.ntu.edu.sg/lib-scholarlycomm/?p=18865).
Complexities in Open Access Discovery InterfacesMichael Habib
“It Isn’t ‘Open’ If You Can’t Find It: New Open Access Discovery Tools that Close the Gap between Readers and Open Content“, Speaker, Charleston Conference – November 9, 2017; Charleston, SC
Abstract: https://2017charlestonconference.sched.com/event/CHqR/it-isnt-open-if-you-cant-find-it-new-open-access-discovery-tools-that-close-the-gap-between-readers-and-open-content
Clarivate as the Citation Provider for ERA presented by
Jean-Francois Desvignes (Solution Consultant, Scientific and Academic Research, Clarivate Analytics) at the Research Support Community Day 2018
Clarivate Analytics was selected in 2017 to become the Citation provider by the Australian Research Council (ARC) for the 2018 Excellence in Research for Australia (ERA) evaluation. We will first highlight the data from the Web of Science that was made available by our team to Australian Higher Education Providers (HEP) for ERA. Then, we will focus on the solutions developed my Clarivate Analytics to support the Australian HEPs when preparing and analysing their data and prior to the submission to the Australian Research Council.
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.
A FAIR Data Sharing Framework for Large-Scale Human Cancer ProteogenomicsBrett Tully
A FAIR Data Sharing Framework for Large-Scale Human Cancer Proteogenomics
Islam M1,2, Christiansen J3, Mahboob S4, Valova V4, Baker M4, Capes-Davis D4, Hains P4, Balleine R1,4, Zhong Q1,4, Reddel R1,4, Robinson P1,4, Tully B4
1 The University of Sydney, Camperdown, Sydney, NSW, 2050, Australia
2 Intersect, Level 13/50 Carrington St, Sydney, NSW, 2000, Australia
3 Queensland Cyber Infrastructure Foundation Ltd, Axon Building 47, University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
4 Children’s Medical Research Institute, Westmead, NSW, 2145, Australia
Background
The ACRF International Centre for the Proteome of Cancer (ProCan) at Children’s Medical Research Institute (CMRI) is an “industrial scale” program specialising in small-sample proteomics analysis from human cancer tissue.
ProCan seeks to generate both a wide and deep analytics pipeline and requires an enabling data framework. The framework must accommodate initial analysis and proteomic profiling of a large number of tumor samples, along with the clinical and demographic information, subsequent multi-omics studies, and any previously recorded responses to treatment. The curated datasets will provide a valuable resource beyond their primary use and ProCan is committed to making its data accessible to collaborators and the wider scientific community.
Objectives
The objective of the project is to an establish efficient, reliable, secure and ethical data sharing and publication framework based on the best practice data sharing principles, such as the FAIR principle. The framework must address various challenges that stem from the scale and complexity of the program, and ProCan’s focus on human-derived data and associated challenges presented in sharing these data while maintaining the privacy of any research participants.
Method
The project adopted a requirements-driven methodology and engaged with a wide range of ProCan stakeholders nationally and internationally. Together, various industrial-scale proteomics data management and sharing scenarios were explored such that robust and ethical sharing of the data would be achieved.
Results
The project developed a data sharing framework based on the FAIR principle that currently forms the basis of ongoing implementation work within the ProCan program.
Presented at the Research Support Community Day by Natasha Simons (Program Leader for Skills, Policy and Resources, Australian National Data Service)
An increasing number of scholarly publishers and journals are implementing policies and procedures that require published articles to be accompanied by the underlying research data. These policies are an important part of the shift toward reproducible research and have been shown to influence researchers’ willingness to share research data to varying extents. However journal data availability policies are highly idiosyncratic, vary in strength from encouraging to mandating data sharing, and are often difficult to interpret. This makes it challenging for researchers to comply, editors to introduce and research support staff to assist. This presentation examined why and how more scholarly publishers/journals are introducing data availability policies and explore the differences in journal data sharing policies, referring to examples. It outlined the challenges of current data policies, what is expected of various stakeholders, and reflect on efforts in Australia to engage stakeholders in conversation to improve data policies including 2017 Social Sciences and Health and Medical roundtables. It concluded with an update on international collaborations that are helping to facilitate wider adoption of clear, consistent policies for publishing research data.
Data citation metrics : best practice to enable new metrics for research dataLe_GFII
Intervention de Nigel Robinson, Director, Content Managment chez Thomson Reuters au Forum du GFII 2015 : http://forum.gfii.fr/forum/les-nouvelles-mesures-de-l-influence-scientifique-l-apport-des-metriques-alternatives-au-pilotage-de-la-recherche
A template for a basic data management plan. Handout to accompany the presentations Introduction to Research Data Management and Preparing Your Research Data for the Future.
From Data Policy Towards FAIR Data For All: How standardised data policies ca...Rebecca Grant
There is evidence that good data practice leads to increased citation, increased reproducibility, increased productivity, reduced harm and costs of biased or non-transparent research, and that it helps researchers with career progression and provides a better return on investment in research funding. In this presentation we will share feedback on data sharing from a survey of more than 11,000 researchers globally, as well as evidence from our own implementation of standardised data policies and the work of the Research Data Alliance’s Data Policy Implementation Interest Group.
Update from Data policy standardisation and implementation IGVarsha Khodiyar
Update given to the Research Data Alliance Plenary 12 joint meeting session: WG FAIRSharing Registry and Data Policy Standardisation and Implementation IG, on Monday 5th November 2018, Gaborone, Botswana
Public access to research results at USDACyndy Parr
An update on public access activities at the National Agricultural Library and next steps, presented 11 January 2017 at the Earth Science Information Partners (ESIP) meeting in Bethesda, Maryland.
In early 2014, we asked science and social science researchers...
• What expectations do the terms publication and peer review raise in reference to data?
• What features would be useful to evaluate the trustworthiness, evaluate the impact, and enhance the prestige of a data publication?
Slides presented during the "Open Access Research Data Sharing Requirements: Are you ready?” on 27 Oct 2016 @ NTU.
Questions and comments from the participants have been posted on Scholarly Communication Blog (https://blogs.ntu.edu.sg/lib-scholarlycomm/?p=18865).
Complexities in Open Access Discovery InterfacesMichael Habib
“It Isn’t ‘Open’ If You Can’t Find It: New Open Access Discovery Tools that Close the Gap between Readers and Open Content“, Speaker, Charleston Conference – November 9, 2017; Charleston, SC
Abstract: https://2017charlestonconference.sched.com/event/CHqR/it-isnt-open-if-you-cant-find-it-new-open-access-discovery-tools-that-close-the-gap-between-readers-and-open-content
Clarivate as the Citation Provider for ERA presented by
Jean-Francois Desvignes (Solution Consultant, Scientific and Academic Research, Clarivate Analytics) at the Research Support Community Day 2018
Clarivate Analytics was selected in 2017 to become the Citation provider by the Australian Research Council (ARC) for the 2018 Excellence in Research for Australia (ERA) evaluation. We will first highlight the data from the Web of Science that was made available by our team to Australian Higher Education Providers (HEP) for ERA. Then, we will focus on the solutions developed my Clarivate Analytics to support the Australian HEPs when preparing and analysing their data and prior to the submission to the Australian Research Council.
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.
A FAIR Data Sharing Framework for Large-Scale Human Cancer ProteogenomicsBrett Tully
A FAIR Data Sharing Framework for Large-Scale Human Cancer Proteogenomics
Islam M1,2, Christiansen J3, Mahboob S4, Valova V4, Baker M4, Capes-Davis D4, Hains P4, Balleine R1,4, Zhong Q1,4, Reddel R1,4, Robinson P1,4, Tully B4
1 The University of Sydney, Camperdown, Sydney, NSW, 2050, Australia
2 Intersect, Level 13/50 Carrington St, Sydney, NSW, 2000, Australia
3 Queensland Cyber Infrastructure Foundation Ltd, Axon Building 47, University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia
4 Children’s Medical Research Institute, Westmead, NSW, 2145, Australia
Background
The ACRF International Centre for the Proteome of Cancer (ProCan) at Children’s Medical Research Institute (CMRI) is an “industrial scale” program specialising in small-sample proteomics analysis from human cancer tissue.
ProCan seeks to generate both a wide and deep analytics pipeline and requires an enabling data framework. The framework must accommodate initial analysis and proteomic profiling of a large number of tumor samples, along with the clinical and demographic information, subsequent multi-omics studies, and any previously recorded responses to treatment. The curated datasets will provide a valuable resource beyond their primary use and ProCan is committed to making its data accessible to collaborators and the wider scientific community.
Objectives
The objective of the project is to an establish efficient, reliable, secure and ethical data sharing and publication framework based on the best practice data sharing principles, such as the FAIR principle. The framework must address various challenges that stem from the scale and complexity of the program, and ProCan’s focus on human-derived data and associated challenges presented in sharing these data while maintaining the privacy of any research participants.
Method
The project adopted a requirements-driven methodology and engaged with a wide range of ProCan stakeholders nationally and internationally. Together, various industrial-scale proteomics data management and sharing scenarios were explored such that robust and ethical sharing of the data would be achieved.
Results
The project developed a data sharing framework based on the FAIR principle that currently forms the basis of ongoing implementation work within the ProCan program.
Presented at the Research Support Community Day by Natasha Simons (Program Leader for Skills, Policy and Resources, Australian National Data Service)
An increasing number of scholarly publishers and journals are implementing policies and procedures that require published articles to be accompanied by the underlying research data. These policies are an important part of the shift toward reproducible research and have been shown to influence researchers’ willingness to share research data to varying extents. However journal data availability policies are highly idiosyncratic, vary in strength from encouraging to mandating data sharing, and are often difficult to interpret. This makes it challenging for researchers to comply, editors to introduce and research support staff to assist. This presentation examined why and how more scholarly publishers/journals are introducing data availability policies and explore the differences in journal data sharing policies, referring to examples. It outlined the challenges of current data policies, what is expected of various stakeholders, and reflect on efforts in Australia to engage stakeholders in conversation to improve data policies including 2017 Social Sciences and Health and Medical roundtables. It concluded with an update on international collaborations that are helping to facilitate wider adoption of clear, consistent policies for publishing research data.
GSmith Springer Nature Data policies and practices: HKU Open Data and Data Pu...GrahamSmith646206
Supporting research data across Springer Nature: joining up policy and practice. Slides from Graham Smith (Research Data Manager, Springer Nature) at HKU Open Data and Data Publishing Seminar, 25th October 2021.
New approaches to data management: supporting FAIR data sharing at Springer N...Varsha Khodiyar
Presentation given at Biocuration 2019 Session 5 (Data standards and ontologies: Making data FAIR)
Abstract:
Since 2016, academic publishers including Springer Nature, Elsevier and Taylor & Francis have been providing standard research data policies to journal authors, reflecting key aspects of the FAIR Principles’ practical applications: sharing data in repositories, using persistent identifiers and citing data appropriately. In spite of the rise of FAIR and good data management practice, recent surveys found that nearly 60% of researchers had never heard of the FAIR Principles, and 46% are not sure how to organise their data in a presentable and useful way. In this presentation we will analyse the results of a white paper which assessed the key challenges faced by researchers in sharing their data, and discuss current initiatives and approaches to support researchers to adopt good data sharing practice.
These include the roll-out of research data policies since 2016, as well as the launch of a Helpdesk service which has provided support to authors and allowed the research data team to capture more granular information on the challenges they face in sharing their data. We will also discuss the development of a third-party curation service which assists authors in depositing their data into appropriate repositories, and drafting data availability statements.
Finally we will assess the impacts of some of these interventions, including an analysis of data availability statements and an overview of the methods authors are currently using to share their data, and how these align with FAIR.
FAIR for the future: embracing all things dataARDC
FAIR for the future: embracing all things data - Natasha Simons, Keith Russell and Liz Stokes, presented at Taylor & Francis Scholarly Summits in Sydney 11 Feb 2019 and Melbourne 14 Feb 2019.
Facilitating good research data management practice as part of scholarly publ...Varsha Khodiyar
Presentation given to the SciDataCon #IDW2018 session: Democratising Data Publishing: A Global Perspective, on Tuesday 6th November 2018, Gaborone, Botswana
Lesson 2 in a set of 10 created by DataONE on Best Practices fo Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
OPEN DATA. The researcher perspective
Preface
Paul Wouters
Professor of Scientometrics,
Director of CWTS,
Leiden University
Wouter Haak
Vice President,
Research Data Management,
Elsevier
A year ago, in April 2016, Leiden University’s Centre for
Science and Technology Studies (CWTS) and Elsevier
embarked on a project to investigate open data practices
at the workbench in academic research. Knowledge
knows no borders, so to understand open data practices
comprehensively the project has been framed from the
outset as a global study. That said, both the European
Union and the Dutch government have formulated the
transformation of the scientific system into an open
innovation system as a formal policy goal. At the time
we started the project, the Amsterdam Call for Action on
Open Science had just been published under the Dutch
presidency of the Council of the European Union. However,
how are policy initiatives for open science related to the
day-to-day practices of researchers and scholars?
Brief summary for the INCF Neuroscience Assembly (https://neuroinformatics.incf.org/2021/program-week-2) of the two sessions run at the RDA Plenary 17th, which FAIRsharing WG has contributed t.
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
Preparing your data for sharing and publishingVarsha Khodiyar
Talk given as part of the MRC Cognition and Brain Sciences Unit Open Science Day on 20th November 2018 , University of Cambridge (https://www.eventbrite.co.uk/e/open-science-day-at-the-mrc-cbu-tickets-50363553745)
Increasing transparency in Medical Education through Open Data Rebecca Grant
Slides presented at the AMEE Virtual Conference 2021, introducing the MedEdPublish platform and data policies. Approaches to sharing sensitive human data, and particulary qualitative data, are discussed.
Research in the time of Covid: Surveying impacts on Early Career ResearchersRebecca Grant
Based on a survey of over 4,500 researchers published in the white paper The State of Open Data 2020, this session will explore the impacts of the pandemic on early career reearchers (ECRs), their research practice, and how they interact with open data. We will discuss the specific challenges reported by ECRs, as well as the gaps in training and support that they have identified that would encourage their sharing and reuse of research data.
Presentation at the E-ARMA conference 2021.
Managing Ireland's Research Data - 3 Research MethodsRebecca Grant
Slides providing an overview of the research methods used in the author's thesis, "Managing Ireland's Research Data: Recognising Roles for Recordkeepers". The methods discussed are online surveys, comparative case studies, and autoethnography.
Licensed as CC-BY.
Do Open data badges influence author behaviour? A case study at Springer NatureRebecca Grant
Digital badges have previously been shown to incentivise journal authors to share their data openly. In this paper we introduce an Open data badging project at the Springer Nature journal BMC Microbiology. The development of the Open data badge is described, as well as the challenges of developing standard badging criteria and ensuring authors’ awareness of the badges. Next steps for the badging project are outlined, which are based on the experiences of the team assessing the badges, the number of badges awarded at the journal to date, and the results of an author survey.
Positioning record keepers as data management professionalsRebecca Grant
Slide deck presented at the Archives and Records Association conference, Glasgow, on Friday 31 August 2018.
Abstract:
This paper will discuss records professionals as people looking after records, with a particular focus on the people who look after research data and the scientific record.
Research data management, data curation and data preservation, whilst long-standing skills areas in their own right, are relatively new skills to many parts of the research
institution, and are most often required within universities to assist researchers in looking after their research outputs. Although there is an increasing need for this type
of support for researchers, there is little consensus on who should have overall responsibility for providing it: the role may be filled by librarians, the research office, IT support, and in many cases university archivists or records managers.
With a lack of consensus on the professional expertise required, those working in data management are investigating new ways to approach issues which are commonly addressed by records professionals in their day-to-day work. Challenges such as
appraisal and preservation have been identified in some research-performing institutions as new problems to be solved, without necessarily acknowledging the existing expertise within the archive and records manager professions.
In this presentation we will describe the current role of records professionals in data management, and the specific expertise and skills which we can bring to this emerging area. We will also present the work that a group of records professionals are
currently undertaking to raise the profile of archivists and records managers as ‘people who can look after data’ in the international data management community.
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).
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
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We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
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Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
A National Approach to Open Data in Ireland: Publishers and Research Data Management
1. A National Approach to Open
Research Data in Ireland:
Publishers and RDM
8 September 2017
Rebecca Grant, Research Data
Manager, Springer Nature
2. 1
Springer Nature is a leading research, educational and professional publisher, providing
quality content to our communities through a range of innovative platforms, products
and services.
Home to brands including Springer, Nature Research, BioMed Central, Palgrave
Macmillan and Scientific American.
As the leading open access publisher, we see the rise of open research in all its
manifestations as one of the major forces reshaping the way that researchers
communicate and collaborate to advance the pace and quality of discovery.
Our focus is on investing in and creating tools, services or training that help the research
community to understand and utilise new ideas and concepts.
3. 2
What challenges do researchers face?
• 64% unsure about open licensing of research data1
• 56% do not use a metadata standard2
• 54% would like more guidance complying with funder policies1
• 54% do not have enough time to make data available1
• 45% unaware of a repository for some of their data3
• 39% uncertain about meeting costs of making data open1,2
1. Treadway et al. (2016). figshare. https://dx.doi.org/10.6084/m9.figshare.4036398.v1 (n= 2061) (& image credit CC BY)
2. Tenopir et al. (2011). PLoS ONE 6(6): e21101. doi:10.1371/journal.pone.0021101 (n=1315)
3. Nature Publishing Group (2014). figshare. http://dx.doi.org/10.6084/ m9.figshare.1234052 (n=387)
4. 3
• Content types e.g. data articles and journals
• Credit and incentives e.g. data citation and data articles
• Encouraging reuse e.g. open licences
• Data quality e.g. data peer review
• Data discoverability e.g. linking data to publications; supporting repositories
• Raising awareness e.g. editorials, outreach
• Guidance and policy e.g. information for authors, policy harmonisation
• Technology e.g. platform developments, repository integration and other
partnerships
What is Springer Nature doing?
5. 4
From our data journal portfolio: Scientific Data
Scientific Data
Part of the Nature Research Group, Scientific
Data is an open-access, online-only journal
for descriptions of scientifically valuable
datasets.
The articles, known as Data Descriptors,
combine traditional narrative content with
curated, structured descriptions (metadata)
of the published data to provide a new
framework for data-sharing and –reuse.
• Broad scope covering physical, life and
quantitative social sciences
• In-house metadata curation
• Data-focused peer-review process
• Supports community data repositories
• Integrated submission of data to general
repositories
8. 7
Research Data Support helpdesk @Springer Nature
Support for editors:
• Identifying and implementing a data policy
• Identifying data repositories for their audience(s)
• Dealing with peer review of sensitive/human data
• Good practice for data-literature integration
Support for authors:
• Information on the data policy of their target journal(s)
• Identifying and using data repositories
• Compliance with funders’ and institutions’ data sharing policies
• Data reporting standards
http://www.springernature.com/gp/group/data-policy/helpdesk
9. 8
Helping researchers find the right repository
• Recommended repositories list (>80 repositories)
• http://www.springernature.com/gp/group/data-policy/repositories
What makes a good data repository?
Data repositories for data supporting peer-reviewed publications generally should:
I. Ensure long-term persistence and preservation of datasets
II. Be recognized by a research community or research institution
III. Provide deposited datasets with stable and persistent identifiers, such as Digital
Object Identifiers (DOIs)
IV. Allow access to data without unnecessary restrictions
V. Provide a clear license or terms of use for deposited datasets
10. 9
Policies need checks and enforcement to be robust and effective1 but
not all researchers and journals have easy access to tools and
resources to share data easily2,3.
To help Springer Nature authors and journals follow good practice in
sharing and archiving of research data, we’re piloting optional data
deposition and curation services.
The services provide secure and private submission of data files, which
are then curated and managed by the Springer Nature Research Data
team for public release.
http://www.springernature.com/gb/group/data-policy/data-support-services
Springer Nature Data Support Services pilot
1. Vines, T. H. et al. Mandated data archiving greatly improves access to research data. FASEB J. fj.12–218164– (2013). doi:10.1096/fj.12-218164
2. Treadway et al. (2016). figshare. https://dx.doi.org/10.6084/m9.figshare.4036398.v1 (n= 2061)
3. Tenopir et al. (2011). PLoS ONE 6(6): e21101. doi:10.1371/journal.pone.0021101 (n=1315)
12. 11
Now includes
• Link to associated, peer-reviewed
publication
• Consistent titles and author names
• Clear citation information
• Files preview-able in browser
• Metadata for each file in the archive
• Contextual information
• Clear license/terms of use
• Dataset description/abstract
• Rich usage statistics
After data curation
15. 14
July 2017
Thank you!
Email: rebecca.grant@springernature.com
research.data@springernature.com
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Editor's Notes
Main findings:
Researchers do share and use one another’s data but lack places to put it.
They would value a high quality data publication