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.
Collaboratively creating a network of ideas, data and softwareAnita de Waard
Anita de Waard discusses collectively creating networks to connect ideas, data, and software. This includes work by Elsevier to build a knowledge graph connecting 14 million articles and ongoing efforts to link papers to datasets through various partnerships. De Waard also discusses evaluating data discoverability and the need to consider software as a knowledge object and pay for open infrastructure through new funding models. The goal is to enable sharing of knowledge globally through interconnected systems and partnerships.
An update on the latest BioSharing work; including work with ELIXIR and NIH BD2K, also our survey to assess user needs (530 replies) and the work on the recommender tool
The value of data curation as part of the publishing processVarsha Khodiyar
Presentation given at Biocuration 2019 Session 5 (Interacting with the Research Community)
Abstract:Journals and publishers have an important role to play in the drive to increase the reproducibility of published science. Since its launch in 2014, the Nature Research journal Scientific Data has established a reputation for publishing data papers (‘Data Descriptors’) that are highly reusable, as evidenced by a strong citation record. One of the ways in which Scientific Data ensures maximum reusability of published data is via the in-house data curation workflow applied to every Data Descriptor. In 2017, Springer Nature launched its Research Data Support (RDS) service to provide data curation expertise to researchers publishing at other Springer Nature journals.
During curation at Scientific Data and RDS, our data editors familiarise themselves with the related manuscript and perform a thorough check of each data archive. This ensures the descriptions in the manuscript match the metadata and data at the data repositories. The curation process facilitates the identification of any discrepancies between the manuscript text and the information held at the data repository.
Over the last year, the curation team have been recording the types of discrepancies rectified as a direct result of our curation process. At Scientific Data approximately 10% of the discrepancies the team find are significant enough to potentially have warranted a formal correction had the issue had not been resolved prior to publication.
In this presentation we give an overview of our observed outcomes from embedding data curation within the publishing process. We describe of how we are monitoring the value of our curation work, and show examples of the types of discrepancy most commonly identified through curation at Scientific Data and RDS.
Management of research data specifically for Engineering and Physical Science. Delivered by Stuart Macdonald at the "Support for Enhancing Research Impact" meeting at the University of Edinburgh on 22 June 2016.
DataONE Education Module 01: Why Data Management?DataONE
Lesson 1 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.
Publishing the Full Research Data LifecycleAnita de Waard
This document discusses strategies for supporting open science through the full research cycle and data/software preservation. It outlines current practices for managing, storing, publishing, and reusing research data and software. It proposes improvements like requiring researchers to post datasets to repositories under embargo linked to any subsequent publications to reduce workload, better track outputs, and improve data linking and availability. The goal is to make data sharing and open science practices more seamless and effective.
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.
Collaboratively creating a network of ideas, data and softwareAnita de Waard
Anita de Waard discusses collectively creating networks to connect ideas, data, and software. This includes work by Elsevier to build a knowledge graph connecting 14 million articles and ongoing efforts to link papers to datasets through various partnerships. De Waard also discusses evaluating data discoverability and the need to consider software as a knowledge object and pay for open infrastructure through new funding models. The goal is to enable sharing of knowledge globally through interconnected systems and partnerships.
An update on the latest BioSharing work; including work with ELIXIR and NIH BD2K, also our survey to assess user needs (530 replies) and the work on the recommender tool
The value of data curation as part of the publishing processVarsha Khodiyar
Presentation given at Biocuration 2019 Session 5 (Interacting with the Research Community)
Abstract:Journals and publishers have an important role to play in the drive to increase the reproducibility of published science. Since its launch in 2014, the Nature Research journal Scientific Data has established a reputation for publishing data papers (‘Data Descriptors’) that are highly reusable, as evidenced by a strong citation record. One of the ways in which Scientific Data ensures maximum reusability of published data is via the in-house data curation workflow applied to every Data Descriptor. In 2017, Springer Nature launched its Research Data Support (RDS) service to provide data curation expertise to researchers publishing at other Springer Nature journals.
During curation at Scientific Data and RDS, our data editors familiarise themselves with the related manuscript and perform a thorough check of each data archive. This ensures the descriptions in the manuscript match the metadata and data at the data repositories. The curation process facilitates the identification of any discrepancies between the manuscript text and the information held at the data repository.
Over the last year, the curation team have been recording the types of discrepancies rectified as a direct result of our curation process. At Scientific Data approximately 10% of the discrepancies the team find are significant enough to potentially have warranted a formal correction had the issue had not been resolved prior to publication.
In this presentation we give an overview of our observed outcomes from embedding data curation within the publishing process. We describe of how we are monitoring the value of our curation work, and show examples of the types of discrepancy most commonly identified through curation at Scientific Data and RDS.
Management of research data specifically for Engineering and Physical Science. Delivered by Stuart Macdonald at the "Support for Enhancing Research Impact" meeting at the University of Edinburgh on 22 June 2016.
DataONE Education Module 01: Why Data Management?DataONE
Lesson 1 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.
Publishing the Full Research Data LifecycleAnita de Waard
This document discusses strategies for supporting open science through the full research cycle and data/software preservation. It outlines current practices for managing, storing, publishing, and reusing research data and software. It proposes improvements like requiring researchers to post datasets to repositories under embargo linked to any subsequent publications to reduce workload, better track outputs, and improve data linking and availability. The goal is to make data sharing and open science practices more seamless and effective.
This document discusses challenges and proposed solutions for improving data sharing, integration, and reuse in research. It outlines the current research data lifecycle and issues like a lack of linking between data and publications. A proposal is made for researchers to publish data in repositories under embargo and automatically notify funders, then link the data to publications. The document also describes efforts by organizations like FORCE11, the National Data Service, and RDA to improve data search, linking, and publishing through collaboration. Key areas discussed include electronic lab notebooks, data repositories, search, linking data to publications, and citation.
Key lecture for the EURO-BASIN Training Workshop on Introduction to Statistical Modelling for Habitat Model Development, 26-28 Oct, AZTI-Tecnalia, Pasaia, Spain (www.euro-basin.eu)
Research data spring: extending the OPD to cover RDMJisc RDM
The research data spring project "Extending the Organisational Profile Document to cover Research Data Management" slides for the third sandpit workshop. Project led by Joy Davidson from the Digital Curation Centre.
FAIR - Working Data - It's not just about FAIR publishing. Presented by John Morrissey from CSIRO at the C3DIS post conference workshop: Managed data – trusted research: an introduction to Research Data Management 31 may 2018 in Melbourne
Standardising research data policies, research data networkJisc RDM
The document discusses standardizing research data policies across journals. It describes an expert group working to develop templates and guidance for data policies. It also discusses a collaboration to implement the Joint Declaration of Data Citation Principles. The group is working with Springer Nature to help standardize their data policies across journals into four main types. The goal is to improve data sharing, citation and reuse.
Now we are six: Integrating Edinburgh DataShare into local and internet in...Robin Rice
#iassist40 presentation, Toronto, 6/6/2014.
Abstract:
Edinburgh DataShare, an institutional data repository, is six years old. It was built as a demonstrator in DSpace by EDINA and Data Library and has been given new life by the University of Edinburgh’s Research Data Management initiative. Following testing by pilot users in various departments last year, DataShare is confirmed as a key RDM service. Since 2008 much external infrastructure has grown around data sharing, and software developers, publishers and librarians are creating new innovations around the sharing and re-use of data daily. How can DataShare be shaped to fit in to this ever-more-sophisticated environment? A number of ongoing developments are helping us integrate the repository in the global context. DataShare is being indexed in Thomson-Reuter’s Data Citation Index. We aspire to attain the Data Seal of Approval for DataShare, a badge that confers trustworthiness through peer review. It is listed in re3data.org and databib registries of data repositories. We offer via extension, peer review of datasets to our depositors by listing journals that publish ‘data papers’ such as F1000 Research. Locally, as Information Services builds new data services such as the Data Store, [private data] Vault and the [metadata-only] Register, we can focus DataShare on its named purpose.
How to get there from here- Research data Managment training. presented by Sue Cook, CSIRO, at the C3DIS post conference workshop; Managed data – trusted research: an introduction to Research Data Management in Melbourne 31st May 2018
This document summarizes work by the RDA/WDS Publishing Data Interest Group to develop a conceptual and practical framework for linking data to literature. It describes the goals of linking research data and publications to increase discoverability, enable proper data reuse, and support attribution. It then outlines a proposed "multi-hub model" infrastructure as an inclusive, standards-based solution. Two key outputs are presented: 1) A prototype "Data-Literature Interlinking" service that has generated over 2 million links, and 2) The Scholix interoperability framework and guidelines for exchanging link data between sources in a standardized way. Participation by sharing link data or helping expand the Scholix standards is encouraged.
Introduction to research data management. Presented by Natasha Simons at the C3DIS post conference workshop: Managed data – trusted research: an introduction to Research Data Management, Melbourne 31st may 2018
Poster RDAP13: A Workflow for Depositing to a Research Data Repository: A Cas...ASIS&T
Betsy Gunia, David Fearon, Benjamin Brosius, Tim DiLauro
JHU Data Management Services
Johns Hopkins University Sheridan Libraries
A Workflow for Depositing to a Research Data Repository: A Case Study for Archiving Publication Data
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13
This is module 2 in the EDI Data Publishing training course. In this module, you will learn about the Environmental Data Initiative, the project that created these trainings. EDI operates the EDI Data Repository and has curators on staff to help scientists deposit their data.
Overcoming obstacles to sharing data about human subjectsRobin Rice
This document discusses overcoming obstacles to sharing human subject data from research. It notes that most data underlying published research is not shared, limiting reproducibility. Common barriers include confidentiality concerns. The document provides recommendations for researchers to plan for data sharing, obtain proper consent, anonymize data when possible, and restrict access when necessary to protect subjects. When data cannot be fully opened, it suggests taking proportionate precautions like reviewing access applications. The dangers of probabilistic data linkage are also discussed. The document promotes using information governance frameworks that follow ethical standards to enable research in the public interest.
The format for the data management plans for PhD students at Wagenigen UR explained. This format was developed by the library in cooperation with the Wageningen Graduate Schools.
This slide shows the set of task groups established under the aegis of the RDA/NISO Privacy Implications of Research Data Sets Interest Group; it was used during the NISO Symposium held on September 11, 2016 in conjunction with International Data Week events in Denver, Colorado.
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.
Winning the Tour de France, Research Data and Data StewardshipAlastair Dunning
Presentation to Sport Data Valley given at TU Delft Library meeting on value of Data Stewardship and Curation for those working with data from elite and public sport
May 2016
This document summarizes the state of open research data by outlining its evolution over time. It begins with centralized data centers in the 1960s and progresses to more collaborative models of data sharing through community agreements and online supplementary materials. The benefits of open data are discussed, including increased reproducibility and citation advantages for authors who share. While open data is ideal, achieving 3-star open standards according to the 5 star scheme is currently realistic. The future may bring stricter funding and publishing requirements to encourage more widespread data sharing.
This document summarizes the state of open research data by outlining its evolution over time. It begins with centralized data centers in the 1960s and progresses to more collaborative models of data sharing through community agreements and online supplementary materials. The benefits of open data are discussed, including increased reproducibility and citation advantages for authors who share. While open data is ideal, achieving 3-star open standards according to the 5 star scheme is currently realistic. The future may bring stricter funding and publishing requirements to encourage more widespread data sharing.
This document discusses challenges and proposed solutions for improving data sharing, integration, and reuse in research. It outlines the current research data lifecycle and issues like a lack of linking between data and publications. A proposal is made for researchers to publish data in repositories under embargo and automatically notify funders, then link the data to publications. The document also describes efforts by organizations like FORCE11, the National Data Service, and RDA to improve data search, linking, and publishing through collaboration. Key areas discussed include electronic lab notebooks, data repositories, search, linking data to publications, and citation.
Key lecture for the EURO-BASIN Training Workshop on Introduction to Statistical Modelling for Habitat Model Development, 26-28 Oct, AZTI-Tecnalia, Pasaia, Spain (www.euro-basin.eu)
Research data spring: extending the OPD to cover RDMJisc RDM
The research data spring project "Extending the Organisational Profile Document to cover Research Data Management" slides for the third sandpit workshop. Project led by Joy Davidson from the Digital Curation Centre.
FAIR - Working Data - It's not just about FAIR publishing. Presented by John Morrissey from CSIRO at the C3DIS post conference workshop: Managed data – trusted research: an introduction to Research Data Management 31 may 2018 in Melbourne
Standardising research data policies, research data networkJisc RDM
The document discusses standardizing research data policies across journals. It describes an expert group working to develop templates and guidance for data policies. It also discusses a collaboration to implement the Joint Declaration of Data Citation Principles. The group is working with Springer Nature to help standardize their data policies across journals into four main types. The goal is to improve data sharing, citation and reuse.
Now we are six: Integrating Edinburgh DataShare into local and internet in...Robin Rice
#iassist40 presentation, Toronto, 6/6/2014.
Abstract:
Edinburgh DataShare, an institutional data repository, is six years old. It was built as a demonstrator in DSpace by EDINA and Data Library and has been given new life by the University of Edinburgh’s Research Data Management initiative. Following testing by pilot users in various departments last year, DataShare is confirmed as a key RDM service. Since 2008 much external infrastructure has grown around data sharing, and software developers, publishers and librarians are creating new innovations around the sharing and re-use of data daily. How can DataShare be shaped to fit in to this ever-more-sophisticated environment? A number of ongoing developments are helping us integrate the repository in the global context. DataShare is being indexed in Thomson-Reuter’s Data Citation Index. We aspire to attain the Data Seal of Approval for DataShare, a badge that confers trustworthiness through peer review. It is listed in re3data.org and databib registries of data repositories. We offer via extension, peer review of datasets to our depositors by listing journals that publish ‘data papers’ such as F1000 Research. Locally, as Information Services builds new data services such as the Data Store, [private data] Vault and the [metadata-only] Register, we can focus DataShare on its named purpose.
How to get there from here- Research data Managment training. presented by Sue Cook, CSIRO, at the C3DIS post conference workshop; Managed data – trusted research: an introduction to Research Data Management in Melbourne 31st May 2018
This document summarizes work by the RDA/WDS Publishing Data Interest Group to develop a conceptual and practical framework for linking data to literature. It describes the goals of linking research data and publications to increase discoverability, enable proper data reuse, and support attribution. It then outlines a proposed "multi-hub model" infrastructure as an inclusive, standards-based solution. Two key outputs are presented: 1) A prototype "Data-Literature Interlinking" service that has generated over 2 million links, and 2) The Scholix interoperability framework and guidelines for exchanging link data between sources in a standardized way. Participation by sharing link data or helping expand the Scholix standards is encouraged.
Introduction to research data management. Presented by Natasha Simons at the C3DIS post conference workshop: Managed data – trusted research: an introduction to Research Data Management, Melbourne 31st may 2018
Poster RDAP13: A Workflow for Depositing to a Research Data Repository: A Cas...ASIS&T
Betsy Gunia, David Fearon, Benjamin Brosius, Tim DiLauro
JHU Data Management Services
Johns Hopkins University Sheridan Libraries
A Workflow for Depositing to a Research Data Repository: A Case Study for Archiving Publication Data
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13
This is module 2 in the EDI Data Publishing training course. In this module, you will learn about the Environmental Data Initiative, the project that created these trainings. EDI operates the EDI Data Repository and has curators on staff to help scientists deposit their data.
Overcoming obstacles to sharing data about human subjectsRobin Rice
This document discusses overcoming obstacles to sharing human subject data from research. It notes that most data underlying published research is not shared, limiting reproducibility. Common barriers include confidentiality concerns. The document provides recommendations for researchers to plan for data sharing, obtain proper consent, anonymize data when possible, and restrict access when necessary to protect subjects. When data cannot be fully opened, it suggests taking proportionate precautions like reviewing access applications. The dangers of probabilistic data linkage are also discussed. The document promotes using information governance frameworks that follow ethical standards to enable research in the public interest.
The format for the data management plans for PhD students at Wagenigen UR explained. This format was developed by the library in cooperation with the Wageningen Graduate Schools.
This slide shows the set of task groups established under the aegis of the RDA/NISO Privacy Implications of Research Data Sets Interest Group; it was used during the NISO Symposium held on September 11, 2016 in conjunction with International Data Week events in Denver, Colorado.
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.
Winning the Tour de France, Research Data and Data StewardshipAlastair Dunning
Presentation to Sport Data Valley given at TU Delft Library meeting on value of Data Stewardship and Curation for those working with data from elite and public sport
May 2016
This document summarizes the state of open research data by outlining its evolution over time. It begins with centralized data centers in the 1960s and progresses to more collaborative models of data sharing through community agreements and online supplementary materials. The benefits of open data are discussed, including increased reproducibility and citation advantages for authors who share. While open data is ideal, achieving 3-star open standards according to the 5 star scheme is currently realistic. The future may bring stricter funding and publishing requirements to encourage more widespread data sharing.
This document summarizes the state of open research data by outlining its evolution over time. It begins with centralized data centers in the 1960s and progresses to more collaborative models of data sharing through community agreements and online supplementary materials. The benefits of open data are discussed, including increased reproducibility and citation advantages for authors who share. While open data is ideal, achieving 3-star open standards according to the 5 star scheme is currently realistic. The future may bring stricter funding and publishing requirements to encourage more widespread data sharing.
The Role of Retention Time in Untargeted MetabolomicsJan Stanstrup
1) The document discusses methods for predicting retention times (RT) in untargeted metabolomics to help identify compounds.
2) It compares existing approaches like prediction from molecular structure (QSRR) and projection from isocratic runs, and presents a new method called PredRet that allows sharing of RT data across chromatographic systems.
3) PredRet builds models to map RTs between systems accurately, which can help distinguish isomers and limit the number of candidate identifications from mass spectral matches alone.
The document provides an overview of data science, artificial intelligence, and machine learning. It discusses the differences between AI and machine learning, as well as what constitutes data science. Examples are given of applying data science in healthcare to study the impact of remote patient monitoring devices and identify high-risk patients. State-of-the-art machine learning techniques like neural networks, deep learning, and deep reinforcement learning are also overviewed. Finally, the document discusses how companies are using data science and AI and provides next steps for learning and applying these fields.
"Open Science, Open Data" training for participants of Software Writing Skills for Your Research - Workshop for Proficient, Helmholtz Centre Potsdam - GFZ German Research Centre for Geosciences, Telegrafenberg, December 16, 2015
Responsible conduct of research: Data ManagementC. Tobin Magle
A presentation for the Food and Nutrition Science Responsible conduct of research class on data management best practices. Covers material in the context of writing a data management plan.
Harnessing Edge Informatics to Accelerate Collaboration in BioPharma (Bio-IT ...Tom Plasterer
As scientists in the life sciences we are trained to pursue singular goals around a publication or a validated target or a drug submission. Our failure rates are exceedingly high especially as we move closer to patients in the attempt to collect sufficient clinical evidence to demonstrate the value of novel therapeutics. This wastes resources as well as time for patients depending upon us for the next breakthrough.
Edge Informatics is an approach to ameliorate these failures. Using both technical and social solutions together knowledge can be shared and leveraged across the drug development process. This is accomplished by making data assets discoverable, accessible, self-described, reusable and annotatable. The Open PHACTS project pioneered this approach and has provided a number of the technical and social solutions to enable Edge Informatics. A number of pre-competitive consortia and some content providers have also embraced this approach, facilitating networks of collaborators within and outside a given organization. When taken together more accurate, timely and inclusive decision-making is fostered.
Reproducibility and Scientific Research: why, what, where, when, who, how Carole Goble
This document discusses the importance of reproducibility in scientific research. It makes three key points:
1. For results to be considered valid, scientific publications should provide clear descriptions of methods and protocols so that other researchers can successfully repeat and extend the work.
2. Many factors can undermine reproducibility, such as publication pressures, poor training, disorganization, and outright fraud. Ensuring reproducible research requires transparency across experimental designs, data, software, and computational workflows.
3. Achieving reproducible science is challenging and poorly incentivized due to the resources and time required to prepare materials for independent verification. Overcoming these issues will require collective effort across the research community.
This document provides an overview of Philip Bourne's early observations and thoughts regarding data management at the NIH. Some of the key points are: 1) Existing data resources are not well understood in terms of how they are used; 2) There is a need to focus on how data will be managed and shared, not just why it should be; 3) There is no NIH-wide sustainability plan for data management; 4) Training in biomedical data science is inconsistent. The document discusses some potential solutions such as establishing a NIH data commons and improving training programs.
Why study Data Sharing? (+ why share your data)Heather Piwowar
A presentation to the DBMI department at the University of Pittsburgh about data sharing and reuse: what this means, why it is important, some of what we’ve learned, and what we still don’t know.
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
The Emerging Discipline of Data Science: Principles and Techniques for Data-Intensive Analysis, Keynote, 2nd Swiss Workshop on Data Science – SDS|2015, Winterthur, Switzerland, 12 June 2015
Abstract and other presentations at: http://michaelbrodie.com/?page_id=17
data management, information management, data, big data, personal organization, organization, file management, scientific research, research, project management, data security, file naming conventions, data management plan,
Frankie Rybicki slide set for Deep Learning in Radiology / MedicineFrank Rybicki
These are my #AI slides for medical deep learning using #radiology and medical imaging examples. Please use them & modify to teach your own group about medical AI.
How do we know what we don't know? Exploring the data and knowledge space th...Maryann Martone
The document discusses the Neuroscience Information Framework (NIF), an initiative that aims to catalog and integrate neuroscience resources and data. NIF surveys the neuroscience resource landscape, currently cataloging over 3000 databases and datasets. It provides semantic integration of these resources through the use of ontologies and allows deep search of aggregated data. However, significant amounts of neuroscience data and resources remain inaccessible in publications, databases, and file drawers. Barriers to data sharing include lack of incentives, standards, and resources. NIF and related efforts aim to develop solutions to make more neuroscience data FAIR - findable, accessible, interoperable, and reusable.
Ohio Center of Excellence in Knowledge-Enabled Computing at Wright State (Kno.e.sis)
Center overview: http://bit.ly/coe-k
Invitation: http://bit.ly/COE-invite
Similar to Who will use the open data? Mark Humphries keynote (20)
Recent national and international mandates and reports seek to promote an open research infrastructure which facilitates easy access to knowledge and information for all. For example, The UK Open Research Data Task Force report, released in February 2019, recommends user-friendly services for research data management and infrastructure to maximise interoperability and discoverability.
Jisc has built the Open Research Hub (JORH), which integrates a repository, preservation, reporting and storage platform. This cloud-based service is a community governed, multi-tenant solution for universities and other research institutions to manage, store, preserve and share their published research data. Based on existing open standards, the service’s open and extensive data model incorporates best practice from across the sector, including DataCite, CrossRef, CERIF, Dublin Core and PREMIS.
While the Hub was built to address the needs of research data curation, its adoption of open, best practice standards means it has the potential to allow the service to handle a much wider range of digital research objects, including Open Access articles, theses and software. The data model, rich messaging layer and an open API facilitate interoperability with other institutional and scholarly communications systems. This provides the potential for the Hub to underpin infrastructure capable of meeting the requirements of an ever-evolving open research agenda.
This talk will introduce some of the key initiatives seeking to shape open research infrastructure and discuss how the Hub’s current and future development is directed towards facilitating open research best practice. Consideration will be given to how the Hub either meets or can meet recent recommendations such as FAIR, Plan S, ORDTF and the COAR’s Next Generation Repositories.
Jisc Research Data Shared Service Open Repositories 2018 PaperJisc RDM
The document discusses Jisc's plans to develop a national research data shared service in the UK. It provides context on open science policies and the need for research data management and preservation. It then summarizes Jisc's proposal to create a multi-tenant research repository with integrated preservation systems. This would provide a scalable, sustainable platform to help universities meet requirements for managing and preserving research outputs including data, software, and publications. The service is currently in development with pilots planned, and would offer repositories, preservation, or an end-to-end solution to members.
Jisc Research Data Shared Service Open Repositories 2018 24x7Jisc RDM
This document discusses the Jisc Research Data Shared Service (RDSS) and its priorities and developments. The RDSS aims to provide a scalable, sustainable, and intuitive shared research data service. It offers three standard service options - an end-to-end service, repository service, and preservation service. The RDSS is working on developing a multi-tenant research repository and integrating with other Jisc services to support the full research lifecycle from publication to preservation. Further developments include preservation action registries and a potential national shared research platform.
Jisc Research Data Shared Service - a Samvera case studyJisc RDM
As part of its Research Data Shared Service (RDSS), Jisc has been developing a repository component as part of its core architecture . Through making an integrated research data management platform available to UK Universities, there is a growing demand from small to medium HEIs for the RDSS to provide a single repository solution that fits their needs for publications and data with workflows for Open Access and REF submissions. To achieve this, the repository must be integrated with other Jisc Open Access services such as Sherpa, Jisc Monitor and Publications router, along with those provided by external stakeholders such as ORCID, Crossref, DataCite and OpenAIRE.
This presentation is a case study in evaluating Samvera for this role, and its suitability as a multi-tenanted, sustainable hybrid repository that is both attractive to researchers and universities and aligns with the broader international objectives of the community, the FAIR agenda and open science.
Building a national Data Repository Data ModellingJisc RDM
This document outlines an agenda for a Jisc workshop on data modelling. The workshop will cover StarUML for data modelling, the Jisc Research Data Shared Service conceptual architecture, the canonical data model on GitHub, modelling for interoperability, making data FAIR according to metadata principles, a recent FAIR practices report, content modelling and content models, mapping between the canonical data model and CERIF standard, and an exercise for participants to build their own content model.
Building a national Data Repository System Integration Architecture OverviewJisc RDM
This document discusses publish-subscribe (pub-sub) messaging and how it was implemented for RDSS integration architecture. Pub-sub messaging uses asynchronous and decoupled integration mechanisms like files, databases or APIs to transmit messages. It outlines the lifecycle of a message and why pub-sub messaging provides benefits like operability, architectural compliance, and reliability. Finally, it provides references to the message specification, structure, transport and application behavior used for the pub-sub implementation.
Building a National Data Service Open Repositories 2018Jisc RDM
This document outlines the agenda and introductory information for a workshop on building a national research data service in the UK. The agenda covers introducing the Jisc Research Data Shared Service (RDSS) and demonstrating its data modeling and system integration architecture. Participants will have interactive sessions on workflows, events, and integrations. Speakers will include representatives from Jisc, Figshare, and Digirati discussing their experiences with RDSS. Jisc aims to create a shared, interoperable research data infrastructure for UK universities to better manage research data across institutions.
The Jisc RDMToolkit document discusses the development of a Research Data Management (RDM) toolkit by Jisc and Research Consulting. It provides a sneak peek of the toolkit, which gathers over 100 RDM resources and arranges them using a research data lifecycle model. The toolkit is built on a WordPress template for easy editing and will be maintained by a working group. It will undergo a thorough review after three years.
Stories from the Field: Data are Messy and that's (kind of) okJisc RDM
This document introduces Jude Towers and David Ellis, who are lecturers focused on quantitative methods and computational social science. They discuss how data can be messy, including inconsistencies in concepts and definitions, difficulties in data collection, and the politics of data cleaning. They argue that while data is imperfect, it is still useful for understanding society when the signal is distinguished from the noise. They provide two examples of working with messy real-world data: administrative health records from the NHS and social science replication problems. Their overall goal is to help people critically engage with quantitative data.
'Making the case for a research data shared service' in the Measuring Success and Changing Culture session Presented during the National RDM Strategies session of the Göttingen-CODATA RDM Symposium 2018
Research Data Shared Service update at DPCJisc RDM
The document discusses the Jisc Research Data Shared Service (RDSS) and its role in coordinating the preservation and sharing of research data. RDSS aims to provide core functionality for researchers to deposit, describe, store, publish, and ensure the integrity of their research data. It will also offer advice and best practices for research data management. The service coordinates efforts across universities and involves partnerships with other organizations to develop shared technology solutions for preserving UK research outputs.
The webinar discussed Jisc's proposal for a Research Data Shared Service (RDSS) to address issues with research data management across UK higher education institutions. The RDSS would provide cost-effective solutions for depositing, describing, storing, publishing, and preserving research data through standardized technology and shared expertise. An alpha version was being piloted with 16 institutions and would include repository, preservation, and advisory services. The goal was to increase access to and reuse of research data while reducing costs and risks for institutions.
Managing data behind creative masterpiecesJisc RDM
The document discusses research data in creative fields and Jisc's Research Data Shared Service (RDSS) to help manage such data. RDSS aims to enable open science through efficient capture, preservation and reuse of research data. It will provide core functions like deposit, description, storage, publication and preservation of data, as well as reporting and advisory services. RDSS addresses key issues in research data management to help reduce costs and risks for researchers and institutions.
This document provides an agenda for a lightning talks session taking place on June 27th, 2017. It lists 8 presenters, their institutions, and the titles of their short presentations. Topics will include the role of archivists in research data management, the HYDRA and SAMVERA platforms, open research at the University of Leeds, the THOR project, shared data center services, monitoring institutional compliance with RDM policy, and understanding what constitutes research data. The document also provides contact information for the session organizer.
Title: Monitoring institutional compliance with RDM policy
database that is used by the team to monitor compliance.
Research Data Network
University of Strathclyde
The chapter Lifelines of National Economy in Class 10 Geography focuses on the various modes of transportation and communication that play a vital role in the economic development of a country. These lifelines are crucial for the movement of goods, services, and people, thereby connecting different regions and promoting economic activities.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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17. A future of “data” labs
Three ways to be a data scientist:
(1)Help out [informatics]
(2)Collaborate [private data sharing]
(3)Independent [use open data]
25. Conclusions
Sharing data takes effort
Effort needs motivation
Knowing who the data is for is part of that motivation
“Data labs” will be who the data is for
26. Who will use the open data?
Mark Humphries
University of Manchester