The RDA started through collaboration between the European Commission, NSF/NIST in the US, and Australia. Various meetings in 2012 led to the decision to call the organization the Research Data Alliance (RDA). The RDA held its first plenary meeting in Gothenburg, Sweden in March 2013, which saw 240 participants. The RDA has since grown to over 1000 members from 55 countries working in various interest groups and working groups to develop infrastructure and standards to enable open sharing of research data.
RDA is an international organization focused on reducing barriers to data sharing and exchange to enable data-driven innovation. It has over 4,300 members from over 110 countries representing various data-related disciplines. RDA develops infrastructure and facilitates collaboration to address issues like reproducibility, data preservation, metadata standards, and more. Members participate in working groups and interest groups to advance these goals through knowledge sharing, discussions, and developing recommendations and outputs. RDA aims to build bridges that enable open data sharing across technologies, disciplines, and countries.
RDAP13 Mark Parsons: The Research Data Alliance: Making Data WorkASIS&T
Mark Parsons, Rensselaer Polytechnic Institute
Mark A. Parsons and Francine Berman: "The Research Data Alliance: Making Data Work"
Panel: Global scientific data infrastructure
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13
The Research Data Alliance (RDA) is an international organization with over 4,500 members from 115 countries that works to reduce barriers to data sharing and exchange. RDA develops infrastructure and standards to facilitate open data sharing across technologies and disciplines to address major challenges. It brings together domain experts in working groups and interest groups to develop recommendations and outputs related to data citation, metadata standards, data types, and more.
RDA is an international organization with over 6,700 members from 136 countries that works to reduce barriers to data sharing and exchange. RDA develops infrastructure and community activities through working groups and interest groups to accelerate data-driven innovation worldwide. Members collaborate on issues like reproducibility, data citation, metadata standards, and more. Individual membership is free and open to anyone interested in open data sharing.
The Research Data Alliance (RDA) is an international organization with over 10,000 members from 145 countries working to build the social and technical infrastructure to enable open sharing of data. Its vision is for researchers to openly share data across technologies, disciplines, and countries to address societal challenges. The RDA has produced 45 flagship recommendations and outputs and has over 100 cases of adoption across domains. It has 95 active working and interest groups focusing on issues like specific domains, data stewardship, and infrastructure.
The Research Data Alliance (RDA) aims to build social and technical bridges that enable open sharing of data. It has over 9,000 members from 137 countries working in 83 groups to address challenges like interoperability, best practices, and more. RDA produces recommendations and specifications to help researchers openly share data across technologies and disciplines to solve societal challenges.
The Research Data Alliance (RDA) is an international organization with over 9,400 members from 137 countries working to build the social and technical infrastructure to enable open sharing of data. Its mission is to reduce barriers to data sharing across technologies, disciplines and countries. RDA has numerous working groups and interest groups addressing challenges such as metadata, citation, preservation, and more. Membership is open and free for individuals and provides opportunities for collaboration.
The RDA started through collaboration between the European Commission, NSF/NIST in the US, and Australia. Various meetings in 2012 led to the decision to call the organization the Research Data Alliance (RDA). The RDA held its first plenary meeting in Gothenburg, Sweden in March 2013, which saw 240 participants. The RDA has since grown to over 1000 members from 55 countries working in various interest groups and working groups to develop infrastructure and standards to enable open sharing of research data.
RDA is an international organization focused on reducing barriers to data sharing and exchange to enable data-driven innovation. It has over 4,300 members from over 110 countries representing various data-related disciplines. RDA develops infrastructure and facilitates collaboration to address issues like reproducibility, data preservation, metadata standards, and more. Members participate in working groups and interest groups to advance these goals through knowledge sharing, discussions, and developing recommendations and outputs. RDA aims to build bridges that enable open data sharing across technologies, disciplines, and countries.
RDAP13 Mark Parsons: The Research Data Alliance: Making Data WorkASIS&T
Mark Parsons, Rensselaer Polytechnic Institute
Mark A. Parsons and Francine Berman: "The Research Data Alliance: Making Data Work"
Panel: Global scientific data infrastructure
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13
The Research Data Alliance (RDA) is an international organization with over 4,500 members from 115 countries that works to reduce barriers to data sharing and exchange. RDA develops infrastructure and standards to facilitate open data sharing across technologies and disciplines to address major challenges. It brings together domain experts in working groups and interest groups to develop recommendations and outputs related to data citation, metadata standards, data types, and more.
RDA is an international organization with over 6,700 members from 136 countries that works to reduce barriers to data sharing and exchange. RDA develops infrastructure and community activities through working groups and interest groups to accelerate data-driven innovation worldwide. Members collaborate on issues like reproducibility, data citation, metadata standards, and more. Individual membership is free and open to anyone interested in open data sharing.
The Research Data Alliance (RDA) is an international organization with over 10,000 members from 145 countries working to build the social and technical infrastructure to enable open sharing of data. Its vision is for researchers to openly share data across technologies, disciplines, and countries to address societal challenges. The RDA has produced 45 flagship recommendations and outputs and has over 100 cases of adoption across domains. It has 95 active working and interest groups focusing on issues like specific domains, data stewardship, and infrastructure.
The Research Data Alliance (RDA) aims to build social and technical bridges that enable open sharing of data. It has over 9,000 members from 137 countries working in 83 groups to address challenges like interoperability, best practices, and more. RDA produces recommendations and specifications to help researchers openly share data across technologies and disciplines to solve societal challenges.
The Research Data Alliance (RDA) is an international organization with over 9,400 members from 137 countries working to build the social and technical infrastructure to enable open sharing of data. Its mission is to reduce barriers to data sharing across technologies, disciplines and countries. RDA has numerous working groups and interest groups addressing challenges such as metadata, citation, preservation, and more. Membership is open and free for individuals and provides opportunities for collaboration.
The Research Data Alliance (RDA) is an international organization with over 9,499 members from 137 countries working to build the social and technical infrastructure to enable open sharing of data. RDA has developed 32 flagship technical specifications and standards, and their recommendations have been adopted in 75 cases across multiple disciplines, organizations, and countries. RDA members collaborate in 85 working and interest groups focused on issues like interoperability, data stewardship, and community needs. The organization's vision is for researchers to openly share data to address societal challenges.
The Research Data Alliance (RDA) is an international organization with over 8,000 members from 137 countries that works to build social and technical bridges to enable open sharing of data. It has produced 30 flagship recommendations and outputs to reduce barriers to data sharing. RDA members collaborate in 100 working and interest groups on challenges such as metadata standards, data citation, and legal interoperability.
The Research Data Alliance (RDA) is an international organization with over 4,600 members from 115 countries that works to reduce barriers to data sharing and exchange. RDA develops infrastructure and facilitates collaboration between members to address challenges related to data reproducibility, preservation, citation, and more. Key activities include the work of working groups and interest groups, as well as developing recommendations and outputs like data standards. RDA's vision is for open sharing of data to help address societal challenges.
The Research Data Alliance (RDA) is an international organization with over 7,900 members from 137 countries that works to build social and technical bridges to enable open sharing of data. It has over 100 working groups and interest groups that have produced 30 flagship recommendations and outputs to reduce barriers to data sharing. Examples of RDA recommendations include the Data Foundation and Terminology Model, PID Information Types API, and the Data Type Registries Model.
The document summarizes Susanna-Assunta Sansone's presentation on enabling FAIR (Findable, Accessible, Interoperable, Reusable) digital resources. It discusses the driving forces behind FAIR including reproducibility crises, new data types, and changing publishing. It then outlines community efforts to develop standards, policies, and tools to improve metadata and data sharing according to FAIR principles. These include domain-specific standards, the FAIRsharing registry, metrics to assess FAIRness, and ongoing work to provide FAIR guidance and services.
The Research Data Alliance (RDA) is an international organization with over 9,600 members from 137 countries working to build the social and technical infrastructure to enable open sharing of data. Its vision is for researchers to openly share data across technologies, disciplines, and countries to address societal challenges. RDA has 85 working and interest groups collaborating to develop recommendations and standards to reduce barriers to data sharing. It has produced 32 flagship recommendations that have been adopted in over 75 cases by organizations worldwide. Membership is open and free for individuals and provides opportunities to work on global data interoperability challenges.
Moving from an IR to a CRIS, the why & howDavid T Palmer
IRs collect, manage and display publications, and their metadata. However, an institution’s research, expertise and capacity is described by more than publications. The HKU Scholars Hub, hosted in DSpace, began as the IR of The University of Hong Kong (HKU) in 2005. Asking for voluntary deposit of publications from HKU academics, it received little notice, and more importantly, little support from University senior management. In 2009 a new HKU initiative, Knowledge Exchange, adopted the Hub as a key vehicle to share knowledge and skill with the community outside HKU. With funding support from the Office of KE, we extended the data model of DSpace to include relational tables on non-publication objects, including people, grants, and patents, holding attributes of these objects, such as co-investigators, co-inventors, co-prize winners, research interests, languages spoken, supervision of postgraduate theses, etc. The DSpace user interface now delivers an integrated search and display on these objects and attributes, as well as on ones newly derived, such as authority work on name disambiguation and synonymy in Roman and Hanzi (漢字), visualizations on networks of co-authors, co-investigators, etc, metrics extracted from external sources such as Scopus, WoS, PubMed, Google Scholar Citations, internal alt-metrics of view and download counts, and more. Beyond the functions of an IR, the Hub now performs as a system for reputation management, impact management, and research networking and profiling -- all of which are concepts included in the broad term, “Current Research Information System” (CRIS). These new objects and attributes curated from several trusted sources, and integrated into the present mashup, contextualize and highlight HKU research, and attract more hits, than an IR with only publications.
The HKU Office of Knowledge Exchange has now funded the modularization of these new HKU features of DSpace. Together with our partner, CINECA of Italy, we are making this work available in open source for the DSpace community.
Cyber Summit 2016: Research Data and the Canadian Innovation ChallengeCybera Inc.
Canada allocates a substantial amount of public funding to research, which is a critical factor in ensuring we remain innovative and competitive. Increasingly this funding is geared to the support and development of digital research infrastructure (DRI), including the underlying networks and the associated data acquisition, storage, analysis and visualization. In order to maximize the benefits of increasingly complex DRI and the research it facilitates, it is important to make sure data is properly stewarded, accessible and reusable. By adopting appropriate approaches to research data management we are better positioned to respond to challenges, such as effectively measuring research impacts, and ensuring the reproducibility, privacy, and security of research outputs.
Research Data Canada (RDC) is a member-driven organization committed to developing a sustainable approach to research data management, one based on interoperability and best practices. This session will provide an update on the efforts of RDC and partner organizations, including: CANARIE, Compute Canada, CARL Portage Network, CASRAI, the TriAgencies, and the Leadership Council for Digital Infrastructure. Intersections with international activities and projects will also be highlighted. These efforts are ultimately designed to faciliate a cohesive national approach to research data management, and one based on a clearly articulated vision for supporting innovation and discovery in Canada.
Mark Leggott is the Executive Director of Research Data Canada.
Crowdsourcing Approaches to Big Data Curation - Rio Big Data MeetupEdward Curry
Data management efforts such as Master Data Management and Data Curation are a popular approach for high quality enterprise data. However, Data Curation can be heavily centralised and labour intensive, where the cost and effort can become prohibitively high. The concentration of data management and stewardship onto a few highly skilled individuals, like developers and data experts, can be a significant bottleneck. This talk explores how to effectively involving a wider community of users within big data management activities. The bottom-up approach of involving crowds in the creation and management of data has been demonstrated by projects like Freebase, Wikipedia, and DBpedia. The talk discusses how crowdsourcing data management techniques can be applied within an enterprise context.
Topics covered include:
- Data Quality And Data Curation
- Crowdsourcing
- Case Studies on Crowdsourced Data Curation
- Setting up a Crowdsourced Data Curation Process
- Linked Open Data Example
- Future Research Challenges
Why Data Citation Currently Misses the PointMark Parsons
This document discusses the need to reconsider data citation practices and proposes alternative use cases beyond traditional bibliographic citations. It argues that data citation has failed to incentivize data sharing or expose hidden data. The document outlines three alternative use cases: 1) attribution and credit for all data contributors, 2) tracking data provenance to ensure reproducibility, and 3) measuring broader impact and return on investment of data. It suggests using digital badges to detail contributor roles and tracking qualitative impacts beyond quantitative metrics. The document concludes that a more nuanced approach is needed to address the diverse ways data is used beyond scholarly literature.
2017 05 03 Implementing Pure at UWA - ANDS Webinar SeriesKatina Toufexis
This document summarizes the University of Western Australia's efforts to consolidate its research data systems. It discusses the migration of datasets from its existing DSpace repository to its new Pure repository to have a single system for publications, theses, and datasets. The migration project timeline and functional requirements are outlined, along with issues encountered with the previous DSpace and Vivo systems. Finally, future plans are mentioned, such as enabling dataset submissions directly in Pure and linking publications, theses, and datasets from the same grants/instruments.
This presentation was provided by Libbie Stephenson, UCLA Social Science Data Archive, during a NISO Virtual Conference on the topic of data curation, held on Wednesday, August 31, 2016
Towards Lightweight Cyber-Physical Energy Systems using Linked Data, the Web ...Edward Curry
Cyber-Physical Energy Systems (CPES) exploit the potential of information technology to boost energy efficiency while minimising environmental impacts. CPES can help manage energy more efficiently by providing a functional view of the entire energy system so that energy activities can be understood, changed, and reinvented to better support sustainable practices. CPES can be applied at different scales from Smart Grids and Smart Cities to Smart Enterprises and Smart Buildings. Significant technical challenges exist in terms of information management, leveraging real-time sensor data, coordination of the various stakeholders to optimize energy usage.
In this talk I describe an approach to overcome these challenges by re-using the Web standards to quickly connect the required systems within a CPES. The resulting lightweight architecture leverages Web technologies including Linked Data, the Web of Things, and Social Media. The paper describes the fundamentals of the approach and demonstrates it within an Enterprise Energy Management scenario smart building.
dkNET Webinar: FAIR Data & Software in the Research Life Cycle 01/22/2021dkNET
Abstract
Good data stewardship is the cornerstone of knowledge, discovery, and innovation in research. The FAIR Data Principles address data creators, stewards, software engineers, publishers, and others to promote maximum use of research data. The principles can be used as a framework for fostering and extending research data services.
This talk will provide an overview of the FAIR principles and the drivers behind their development by a broad community of international stakeholders. We will explore a range of topics related to putting FAIR data into practice, including how and where data can be described, stored, and made discoverable (e.g., data repositories, metadata); methods for identifying and citing data; interoperability of (meta)data; best-practice examples; and tips for enabling data reuse (e.g., data licensing). Practical examples of how FAIR is applied will be provided along the way.
Presenter: Christopher Erdmann, Engagement, support, and training expert on the NHLBI BioData Catalyst project at University of North Carolina Renaissance Computing Institute
dkNET Webinars Information: https://dknet.org/about/webinar
Challenges for research support - Sarah Jones, University of Glasgow, Digital...Mari Tinnemans
This document provides guidance on developing research data management services at universities. It discusses 10 key points: 1) Understanding current research data practices, 2) Deciding what services are needed, 3) Balancing the needs of stakeholders, 4) Securing input and buy-in, 5) Defining roles and responsibilities, 6) Positioning support appropriately, 7) Balancing internal and external provision, 8) Being agile and adaptable to change, 9) Linking systems to integrate services, and 10) Planning for long-term sustainability. The overall message is that developing effective RDM requires understanding user needs, engaging stakeholders, and continually adapting services.
This presentation uses a long-term case study to explore the socio-scientific aspects influencing what data products are created and made available for use. We examine two major satellite remote-sensing product collections from the National Snow and Ice Data Center—one on sea ice extent and another on Greenland ice sheet melt. We examine how the products and their curation have evolved over time in response to environmental events and increasing scientific and public demand over several decades. The products have evolved in conjunction with the needs of a changing and expanding designated user community. These changes in the user community were driven by increased interest in the Arctic partly because of the rapid change in the Arctic as characterized in these data, but also because of the increasing awareness (and controversy) around climate change and its impact.
We find that a data product development cycle supported by a data product team with multiple perspectives is key to mobilizing scientific knowledge to multiple stakeholders. Furthermore, the expertise and approaches to making data open and truly useful must continually adapt to new perceptions, needs, and events. Effective data access is an ongoing process, not a one-time event.
References
Baker K S; Duerr, R E; and Parsons, M A 2016 Scientific knowledge mobilization: Co-evolution of data products and designated communities. International Journal of Digital Curation 10 (2): 110-135. http://dx.doi.org/doi:10.2218/ijdc.v10i2.346
The document discusses open data sharing and the Research Data Alliance (RDA). RDA aims to build social and technical bridges to enable open sharing of data across disciplines. It has over 3700 members from 110 countries working in 60+ groups. RDA addresses issues like interoperability standards, data citation practices, and workforce training to facilitate greater data access and use. The presentation highlights several RDA working groups focusing on specific domains like wheat research and chemistry. It emphasizes that open problem solving and involving stakeholders are key to making data infrastructure successful.
The Research Data Alliance (RDA) is an international organization with over 9,499 members from 137 countries working to build the social and technical infrastructure to enable open sharing of data. RDA has developed 32 flagship technical specifications and standards, and their recommendations have been adopted in 75 cases across multiple disciplines, organizations, and countries. RDA members collaborate in 85 working and interest groups focused on issues like interoperability, data stewardship, and community needs. The organization's vision is for researchers to openly share data to address societal challenges.
The Research Data Alliance (RDA) is an international organization with over 8,000 members from 137 countries that works to build social and technical bridges to enable open sharing of data. It has produced 30 flagship recommendations and outputs to reduce barriers to data sharing. RDA members collaborate in 100 working and interest groups on challenges such as metadata standards, data citation, and legal interoperability.
The Research Data Alliance (RDA) is an international organization with over 4,600 members from 115 countries that works to reduce barriers to data sharing and exchange. RDA develops infrastructure and facilitates collaboration between members to address challenges related to data reproducibility, preservation, citation, and more. Key activities include the work of working groups and interest groups, as well as developing recommendations and outputs like data standards. RDA's vision is for open sharing of data to help address societal challenges.
The Research Data Alliance (RDA) is an international organization with over 7,900 members from 137 countries that works to build social and technical bridges to enable open sharing of data. It has over 100 working groups and interest groups that have produced 30 flagship recommendations and outputs to reduce barriers to data sharing. Examples of RDA recommendations include the Data Foundation and Terminology Model, PID Information Types API, and the Data Type Registries Model.
The document summarizes Susanna-Assunta Sansone's presentation on enabling FAIR (Findable, Accessible, Interoperable, Reusable) digital resources. It discusses the driving forces behind FAIR including reproducibility crises, new data types, and changing publishing. It then outlines community efforts to develop standards, policies, and tools to improve metadata and data sharing according to FAIR principles. These include domain-specific standards, the FAIRsharing registry, metrics to assess FAIRness, and ongoing work to provide FAIR guidance and services.
The Research Data Alliance (RDA) is an international organization with over 9,600 members from 137 countries working to build the social and technical infrastructure to enable open sharing of data. Its vision is for researchers to openly share data across technologies, disciplines, and countries to address societal challenges. RDA has 85 working and interest groups collaborating to develop recommendations and standards to reduce barriers to data sharing. It has produced 32 flagship recommendations that have been adopted in over 75 cases by organizations worldwide. Membership is open and free for individuals and provides opportunities to work on global data interoperability challenges.
Moving from an IR to a CRIS, the why & howDavid T Palmer
IRs collect, manage and display publications, and their metadata. However, an institution’s research, expertise and capacity is described by more than publications. The HKU Scholars Hub, hosted in DSpace, began as the IR of The University of Hong Kong (HKU) in 2005. Asking for voluntary deposit of publications from HKU academics, it received little notice, and more importantly, little support from University senior management. In 2009 a new HKU initiative, Knowledge Exchange, adopted the Hub as a key vehicle to share knowledge and skill with the community outside HKU. With funding support from the Office of KE, we extended the data model of DSpace to include relational tables on non-publication objects, including people, grants, and patents, holding attributes of these objects, such as co-investigators, co-inventors, co-prize winners, research interests, languages spoken, supervision of postgraduate theses, etc. The DSpace user interface now delivers an integrated search and display on these objects and attributes, as well as on ones newly derived, such as authority work on name disambiguation and synonymy in Roman and Hanzi (漢字), visualizations on networks of co-authors, co-investigators, etc, metrics extracted from external sources such as Scopus, WoS, PubMed, Google Scholar Citations, internal alt-metrics of view and download counts, and more. Beyond the functions of an IR, the Hub now performs as a system for reputation management, impact management, and research networking and profiling -- all of which are concepts included in the broad term, “Current Research Information System” (CRIS). These new objects and attributes curated from several trusted sources, and integrated into the present mashup, contextualize and highlight HKU research, and attract more hits, than an IR with only publications.
The HKU Office of Knowledge Exchange has now funded the modularization of these new HKU features of DSpace. Together with our partner, CINECA of Italy, we are making this work available in open source for the DSpace community.
Cyber Summit 2016: Research Data and the Canadian Innovation ChallengeCybera Inc.
Canada allocates a substantial amount of public funding to research, which is a critical factor in ensuring we remain innovative and competitive. Increasingly this funding is geared to the support and development of digital research infrastructure (DRI), including the underlying networks and the associated data acquisition, storage, analysis and visualization. In order to maximize the benefits of increasingly complex DRI and the research it facilitates, it is important to make sure data is properly stewarded, accessible and reusable. By adopting appropriate approaches to research data management we are better positioned to respond to challenges, such as effectively measuring research impacts, and ensuring the reproducibility, privacy, and security of research outputs.
Research Data Canada (RDC) is a member-driven organization committed to developing a sustainable approach to research data management, one based on interoperability and best practices. This session will provide an update on the efforts of RDC and partner organizations, including: CANARIE, Compute Canada, CARL Portage Network, CASRAI, the TriAgencies, and the Leadership Council for Digital Infrastructure. Intersections with international activities and projects will also be highlighted. These efforts are ultimately designed to faciliate a cohesive national approach to research data management, and one based on a clearly articulated vision for supporting innovation and discovery in Canada.
Mark Leggott is the Executive Director of Research Data Canada.
Crowdsourcing Approaches to Big Data Curation - Rio Big Data MeetupEdward Curry
Data management efforts such as Master Data Management and Data Curation are a popular approach for high quality enterprise data. However, Data Curation can be heavily centralised and labour intensive, where the cost and effort can become prohibitively high. The concentration of data management and stewardship onto a few highly skilled individuals, like developers and data experts, can be a significant bottleneck. This talk explores how to effectively involving a wider community of users within big data management activities. The bottom-up approach of involving crowds in the creation and management of data has been demonstrated by projects like Freebase, Wikipedia, and DBpedia. The talk discusses how crowdsourcing data management techniques can be applied within an enterprise context.
Topics covered include:
- Data Quality And Data Curation
- Crowdsourcing
- Case Studies on Crowdsourced Data Curation
- Setting up a Crowdsourced Data Curation Process
- Linked Open Data Example
- Future Research Challenges
Why Data Citation Currently Misses the PointMark Parsons
This document discusses the need to reconsider data citation practices and proposes alternative use cases beyond traditional bibliographic citations. It argues that data citation has failed to incentivize data sharing or expose hidden data. The document outlines three alternative use cases: 1) attribution and credit for all data contributors, 2) tracking data provenance to ensure reproducibility, and 3) measuring broader impact and return on investment of data. It suggests using digital badges to detail contributor roles and tracking qualitative impacts beyond quantitative metrics. The document concludes that a more nuanced approach is needed to address the diverse ways data is used beyond scholarly literature.
2017 05 03 Implementing Pure at UWA - ANDS Webinar SeriesKatina Toufexis
This document summarizes the University of Western Australia's efforts to consolidate its research data systems. It discusses the migration of datasets from its existing DSpace repository to its new Pure repository to have a single system for publications, theses, and datasets. The migration project timeline and functional requirements are outlined, along with issues encountered with the previous DSpace and Vivo systems. Finally, future plans are mentioned, such as enabling dataset submissions directly in Pure and linking publications, theses, and datasets from the same grants/instruments.
This presentation was provided by Libbie Stephenson, UCLA Social Science Data Archive, during a NISO Virtual Conference on the topic of data curation, held on Wednesday, August 31, 2016
Towards Lightweight Cyber-Physical Energy Systems using Linked Data, the Web ...Edward Curry
Cyber-Physical Energy Systems (CPES) exploit the potential of information technology to boost energy efficiency while minimising environmental impacts. CPES can help manage energy more efficiently by providing a functional view of the entire energy system so that energy activities can be understood, changed, and reinvented to better support sustainable practices. CPES can be applied at different scales from Smart Grids and Smart Cities to Smart Enterprises and Smart Buildings. Significant technical challenges exist in terms of information management, leveraging real-time sensor data, coordination of the various stakeholders to optimize energy usage.
In this talk I describe an approach to overcome these challenges by re-using the Web standards to quickly connect the required systems within a CPES. The resulting lightweight architecture leverages Web technologies including Linked Data, the Web of Things, and Social Media. The paper describes the fundamentals of the approach and demonstrates it within an Enterprise Energy Management scenario smart building.
dkNET Webinar: FAIR Data & Software in the Research Life Cycle 01/22/2021dkNET
Abstract
Good data stewardship is the cornerstone of knowledge, discovery, and innovation in research. The FAIR Data Principles address data creators, stewards, software engineers, publishers, and others to promote maximum use of research data. The principles can be used as a framework for fostering and extending research data services.
This talk will provide an overview of the FAIR principles and the drivers behind their development by a broad community of international stakeholders. We will explore a range of topics related to putting FAIR data into practice, including how and where data can be described, stored, and made discoverable (e.g., data repositories, metadata); methods for identifying and citing data; interoperability of (meta)data; best-practice examples; and tips for enabling data reuse (e.g., data licensing). Practical examples of how FAIR is applied will be provided along the way.
Presenter: Christopher Erdmann, Engagement, support, and training expert on the NHLBI BioData Catalyst project at University of North Carolina Renaissance Computing Institute
dkNET Webinars Information: https://dknet.org/about/webinar
Challenges for research support - Sarah Jones, University of Glasgow, Digital...Mari Tinnemans
This document provides guidance on developing research data management services at universities. It discusses 10 key points: 1) Understanding current research data practices, 2) Deciding what services are needed, 3) Balancing the needs of stakeholders, 4) Securing input and buy-in, 5) Defining roles and responsibilities, 6) Positioning support appropriately, 7) Balancing internal and external provision, 8) Being agile and adaptable to change, 9) Linking systems to integrate services, and 10) Planning for long-term sustainability. The overall message is that developing effective RDM requires understanding user needs, engaging stakeholders, and continually adapting services.
This presentation uses a long-term case study to explore the socio-scientific aspects influencing what data products are created and made available for use. We examine two major satellite remote-sensing product collections from the National Snow and Ice Data Center—one on sea ice extent and another on Greenland ice sheet melt. We examine how the products and their curation have evolved over time in response to environmental events and increasing scientific and public demand over several decades. The products have evolved in conjunction with the needs of a changing and expanding designated user community. These changes in the user community were driven by increased interest in the Arctic partly because of the rapid change in the Arctic as characterized in these data, but also because of the increasing awareness (and controversy) around climate change and its impact.
We find that a data product development cycle supported by a data product team with multiple perspectives is key to mobilizing scientific knowledge to multiple stakeholders. Furthermore, the expertise and approaches to making data open and truly useful must continually adapt to new perceptions, needs, and events. Effective data access is an ongoing process, not a one-time event.
References
Baker K S; Duerr, R E; and Parsons, M A 2016 Scientific knowledge mobilization: Co-evolution of data products and designated communities. International Journal of Digital Curation 10 (2): 110-135. http://dx.doi.org/doi:10.2218/ijdc.v10i2.346
The document discusses open data sharing and the Research Data Alliance (RDA). RDA aims to build social and technical bridges to enable open sharing of data across disciplines. It has over 3700 members from 110 countries working in 60+ groups. RDA addresses issues like interoperability standards, data citation practices, and workforce training to facilitate greater data access and use. The presentation highlights several RDA working groups focusing on specific domains like wheat research and chemistry. It emphasizes that open problem solving and involving stakeholders are key to making data infrastructure successful.
CoBRA guideline : a tool to facilitate sharing, reuse, and reproducibility of...Research Data Alliance
CoBRA is a reporting guideline that provides a standardized way to cite bioresources in scientific publications. It aims to improve transparency, openness, and reproducibility of research. The guideline recommends citing each bioresource used in the methods section with its name, ID, organization, number of access, and date of last access. Adding a [BIORESOURCE] tag helps track bioresource use. CoBRA endorsement by organizations like EQUATOR and BBMRI-ERIC aims to facilitate its wider adoption.
The document summarizes the evolution of data citation practices over time. It discusses how data citation was initially part of literature but became more complex with digital data. Early efforts in the 1990s-2000s had little traction. Starting in the mid-2000s, multiple disciplines began developing their own data citation approaches and guidelines, with DOIs becoming a major driver. There is now a consensus phase with joint principles being developed, though implementation is just beginning and will require local cultural changes. The document provides examples of how data is currently cited and discusses best practices around identifiers, versions, and microcitations.
The document provides an overview of the Research Data Alliance (RDA). Some key points:
- RDA builds social and technical bridges to enable open sharing of data across technologies, disciplines, and countries. It has over 3,700 members from 110 countries.
- RDA has 65+ working and interest groups that create standards, best practices, and other resources in 12-18 months to accelerate data sharing. This includes work on data citation principles, agriculture data, and more.
- RDA plays a role in connecting data initiatives at multiple scales from local to global. National groups support local participation in RDA to amplify effects for both national and international communities.
Efficient and effective: can we combine both to realize high-value, open, sca...Research Data Alliance
The document discusses the INDIGO-DataCloud project, which aims to develop an open source cloud platform for computing and data management tailored for science. It seeks to address gaps in interoperability, scalability, and data handling across public and private clouds. The project defined requirements from various scientific communities and developed components implementing its architecture to provide solutions for distributed computing and data resources.
OpenAIRE and Eudat services and tools to support FAIR DMP implementation Research Data Alliance
The document provides an overview of the Open Research Data Pilot, the data management plan, and OPENAIRE tools and services to support implementation of FAIR data management plans. It discusses the aims of the Open Research Data Pilot, which Horizon 2020 projects are required to participate, and the types of data that must be deposited. It also covers topics like creating a data management plan, selecting a repository, making data FAIR, and OPENAIRE support resources like briefing papers, webinars, and the Zenodo repository.
The Research Data Alliance (RDA) is an international organization with over 4,900 members from 118 countries that works to reduce barriers to data sharing and exchange. RDA develops infrastructure and holds working groups to develop recommendations and standards to facilitate open data sharing across technologies and disciplines. Key activities of RDA include developing recommendations for data citation, metadata standards, and repository platforms. RDA outputs include specifications, code, policies and practices to enable greater data interoperability, discoverability, and reuse globally.
This document discusses data curation roles and education. It outlines a study to identify key responsibilities of data curators through a content analysis of job descriptions and interviews. The study aims to develop a glossary to better define the profession and support curriculum development. It finds that data curation skills are evolving rapidly, outpacing higher education. While some library and information science programs provide data curation education, challenges include traditionally attracting humanities students rather than those with science backgrounds needed for the field.
The Research Data Alliance (RDA) is an international organization with over 5,100 members from 122 countries that works to reduce barriers to data sharing and exchange. RDA develops infrastructure and facilitates collaboration between domain experts, legal and policy professionals, and technologists to address challenges related to reproducibility, data preservation, metadata standards, and more. Key activities of RDA include the work of volunteer working and interest groups, developing recommendations and outputs to enable open data sharing, and providing a global network for professionals involved in data-driven research and innovation.
Research Data Alliance in a nutshell - Fotis KarayannisBlue BRIDGE
The Research Data Alliance (RDA) is an international member-based organization focused on building the social and technical infrastructure to enable open sharing of data. With over 5,700 members from 128 countries, RDA works to reduce barriers to data sharing through the development of standards, best practices, and other outputs. Key activities of RDA include the work of various working groups and interest groups focused on issues like data citation, metadata standards, and legal interoperability. RDA has produced several recommendations that have been adopted by organizations to facilitate greater data sharing and reuse.
RDA is an international organization focused on reducing barriers to data sharing. With over 6,300 members from 132 countries, RDA develops infrastructure and standards to facilitate open data sharing across technologies and disciplines. RDA produces recommendations and outputs through working groups to address issues like data citation, metadata standards, and legal interoperability. RDA's goal is to enable researchers and innovators to openly share data and address global challenges.
The Research Data Alliance (RDA) is an international organization with over 5,600 members from 126 countries that works to reduce barriers to data sharing and exchange. RDA develops infrastructure and facilitates collaboration between domain experts, legal and policy professionals, and technologists to address challenges related to data reproducibility, preservation, citation, and more. Key activities of RDA include the work of self-organized working groups and interest groups, as well as developing recommendations and outputs to enable open sharing of research data across technologies and disciplines.
RDA is an international organization focused on reducing barriers to data sharing. With over 6,000 members from 130 countries, RDA develops infrastructure and standards to make data more accessible and reusable. RDA members participate in working groups that address issues like metadata, attribution, and interoperability. RDA produces recommendations and outputs that are adopted by data professionals, organizations, and projects worldwide to improve data practices.
RDA is an international organization focused on reducing barriers to data sharing. It has over 4,700 individual members from 117 countries working in various domains and disciplines. RDA develops infrastructure and standards to facilitate open data sharing through working groups and recommendations. Its goals are to address challenges around reproducibility, data citation, metadata standards, and more to achieve its vision of open data sharing to solve societal problems.
RDA is an international organization focused on reducing barriers to data sharing. It has over 6,100 members from 130 countries working to build social and technical bridges for open sharing of data. RDA develops infrastructure and holds working groups to tackle issues like standards, best practices, and data policies in order to accelerate data-driven innovation worldwide.
RDA is an international organization focused on data sharing and exchange. It has over 4,100 members from over 110 countries working to reduce barriers to data sharing across disciplines. RDA develops infrastructure and standards to enable open data sharing through working groups. Its goals are to address challenges like reproducibility, data preservation, and metadata. RDA outputs include recommendations on data citation, registries, and repository certification to facilitate greater data access and use.
RDA is an international organization focused on data sharing and exchange. It has over 4,100 members from over 110 countries working to reduce barriers to data sharing across disciplines. RDA develops infrastructure and standards to enable open data sharing through working groups. Its goals are to address challenges like reproducibility, data preservation, and metadata. RDA outputs include recommendations on data citation, registries, and repository certification to facilitate greater data access and use.
RDA is an international organization focused on building social and technical infrastructure to enable open sharing of research data across technologies, disciplines, and borders. It has over 5,700 members from 128 countries working in groups to develop recommendations and standards to reduce barriers to data sharing. Key activities of RDA include developing recommendations for data citation, metadata standards, and domain-specific data sharing best practices to facilitate greater access, use, and reuse of research data.
RDA is an international organization focused on data sharing and exchange. It has over 4,200 members from over 110 countries working to reduce barriers to data sharing across disciplines. RDA develops infrastructure and standards to enable open data sharing through working groups. Its goals are to address challenges like reproducibility, data preservation, and metadata. Members come from academia, government, industry and collaborate on technical solutions and social aspects of data stewardship.
RDA is an international organization focused on reducing barriers to data sharing and exchange to enable open sharing of data across technologies and disciplines. It has over 4,000 members from over 110 countries representing various data-related fields. RDA develops infrastructure and standards to facilitate data sharing through working groups that address issues like metadata, data citation, and interoperability. The organization produces recommendations and other outputs aimed at making data discovery, access, use and reuse more reliable for researchers and innovators worldwide.
RDA is an international organization focused on data sharing and exchange. It has over 4,300 members from over 110 countries working to reduce barriers to data sharing across disciplines. RDA develops infrastructure and standards to enable open data sharing through working groups. Its goals are to address challenges like reproducibility, data preservation, and metadata. Members come from academia, government, industry and collaborate on technical solutions and policies to facilitate global data collaboration.
The Research Data Alliance (RDA) is an international organization focused on reducing barriers to data sharing and exchange to facilitate data-driven innovation. With over 3,200 members from over 100 countries, RDA includes data professionals from academia, libraries, science, and more. RDA develops infrastructure like technical standards and best practices to enable open sharing of data across technologies and disciplines. Members collaborate in Working Groups and Interest Groups to tackle challenges like reproducibility, data preservation, metadata standards, and more.
The Research Data Alliance (RDA) is an international organization focused on data sharing and exchange. It has over 3,200 members from over 100 countries, representing data professionals from academia, libraries, science, and more. RDA builds connections to enable open data sharing across technologies and disciplines to address societal challenges. Members collaborate through Working Groups and Interest Groups to develop recommendations and standards on issues like metadata, data citation, and interoperability to facilitate global data sharing.
The document discusses the Research Data Alliance (RDA), an international organization focused on data sharing. It provides information on RDA's vision, mission, members, activities, and outputs. RDA has over 6,400 members from 133 countries working in groups to develop infrastructure and standards to facilitate open data sharing across disciplines. The document outlines the various domain-specific and cross-cutting working groups and interest groups within RDA addressing issues like metadata, data citation, and interoperability.
The Research Data Alliance (RDA) is an international organization focused on data sharing infrastructure and community activities. It has over 3,200 members from over 100 countries, representing data professionals from academia, libraries, earth sciences, astronomy and other disciplines. RDA develops recommendations and standards to reduce barriers to data sharing through working groups. It aims to enable open sharing of data to address societal challenges. Members collaborate on issues like reproducibility, data preservation, and metadata through regional and global activities. RDA membership is free and open to any individual or organization with an interest in data sharing.
The Research Data Alliance (RDA) is an international organization with over 7,600 members from 137 countries working to build the social and technical infrastructure to enable open sharing of data. RDA has produced 30 flagship recommendations and outputs to reduce barriers to data sharing, which have been adopted in over 75 cases across multiple disciplines and countries. RDA members collaborate in 94 working and interest groups to develop solutions to challenges related to data sharing, interoperability, and infrastructure.
The Research Data Alliance (RDA) is an international organization with over 7,500 members from 137 countries that works to build social and technical bridges to enable open sharing of data. RDA has 95 working groups and interest groups addressing challenges in domains like agriculture, health, geospatial and more. The groups have produced 28 flagship outputs including data standards and best practices. RDA brings together researchers, engineers, and data professionals to develop infrastructure and activities that reduce barriers to data sharing.
The Research Data Alliance (RDA) is an international organization with over 7,300 members from 137 countries working to build the social and technical infrastructure to enable open sharing of data. RDA has produced 25 flagship recommendations and outputs that have been adopted in over 75 cases to reduce barriers to data sharing. RDA members collaborate in 94 groups to develop solutions to challenges in domains like agriculture, health, geospatial and more.
Similar to Removing Barriers to Data Sharing: the Research Data Alliance (20)
The Research Data Alliance (RDA) is an international organization with over 11,000 members from 145 countries working to build the social and technical infrastructure to enable open sharing and re-use of research data across technologies, disciplines, and borders. RDA has 36 working groups and 57 interest groups addressing challenges in domains like agriculture, health, materials science, and more. It has produced 50 technical specifications and standards to reduce barriers to data sharing.
The Research Data Alliance (RDA) is an international organization with over 10,000 members from 145 countries that works to reduce barriers to data sharing and exchange. RDA brings together researchers, scientists, and data professionals through Working Groups and Interest Groups to develop standards and best practices for data infrastructure and sharing. RDA has produced 50 outputs including technical specifications and has groups working on issues across multiple disciplines.
The Research Data Alliance (RDA) is an international organization focused on building the social and technical infrastructure to enable open sharing of data. It has over 10,000 individual members from 144 countries collaborating in Working and Interest Groups to develop recommendations and standards to reduce barriers to data sharing. Some of RDA's achievements include 47 flagship outputs, 100+ adoption cases, and 93 active groups addressing challenges such as metadata, repositories, legal issues, and more. The ultimate goal is to allow researchers and innovators to openly share data across technologies and disciplines to address societal challenges.
The Research Data Alliance (RDA) is an international organization with over 10,000 members from 145 countries working to build the social and technical infrastructure to enable open sharing of data. It has 98 working groups and interest groups addressing challenges such as interoperability, data citation, metadata standards, and skills training. The RDA produces recommendations and outputs that are adopted by data repositories, domain organizations, and research communities to reduce barriers to data sharing and exchange.
The Research Data Alliance (RDA) is an international organization with over 10,000 members from 145 countries working to build the social and technical infrastructure to enable open sharing of data. RDA has 91 working groups and interest groups focused on issues like different academic disciplines, legal and technical interoperability, and community needs. The organization has produced 37 flagship recommendations and outputs that have been adopted over 100 times to help reduce barriers to sharing data internationally.
The Research Data Alliance (RDA) is an international organization with over 10,000 members from 144 countries working to build the social and technical infrastructure to enable open sharing of data. Its vision is for researchers to openly share data across technologies, disciplines, and countries to address societal challenges. RDA has over 100 groups working on data interoperability issues and has produced 37 flagship outputs, including technical specifications, with over 100 adoption cases in various organizations and disciplines.
The Research Data Alliance (RDA) is an international organization with over 9,859 members from 144 countries working to build the social and technical infrastructure to enable open sharing of data. Its vision is for researchers to openly share data across technologies, disciplines and countries to address societal challenges. RDA has 85 groups working on data interoperability challenges through Working Groups and Interest Groups. It has produced 32 outputs including technical specifications and seen adoption in over 100 cases. RDA membership is open and free for individuals and provides benefits such as networking and skills development, while organizational membership provides additional benefits such as influencing RDA activities.
The Research Data Alliance (RDA) aims to facilitate data sharing across disciplines to address societal challenges. Individuals are encouraged to engage with RDA to contribute their expertise to discussions and recommendations, access an international network, receive updates on RDA's work, participate in meetings, and gain experience in all stages of the data lifecycle. RDA benefits from individual participation, as individuals bring ideas, problems, and solutions to create a valuable global community focused on reducing barriers to data sharing.
The Research Data Alliance (RDA) aims to facilitate data sharing across disciplines to address societal challenges. Individuals are encouraged to engage with RDA to contribute their expertise to discussions and recommendations, access an international network, receive updates on RDA's work, participate in meetings, and gain experience in all stages of the data lifecycle. RDA benefits from individual participation, as individuals bring ideas, problems, and solutions to create a valuable global community focused on reducing barriers to data sharing.
The document discusses the value of research infrastructure providers engaging with the Research Data Alliance (RDA). It outlines that RDA works to enable open sharing of research data globally across disciplines to address societal challenges. As research is global, infrastructure providers need globally compatible services, and RDA ensures this. The document provides reasons for providers to engage with RDA, such as access to an international network and opportunities to collaborate on data standards. It also describes ways providers can engage, such as joining RDA groups or attending meetings.
The Research Data Alliance (RDA) is an international organization with over 8,900 members from 137 countries working to build the social and technical infrastructure to enable open sharing of data. The RDA has developed 32 flagship recommendations and specifications to reduce barriers to data sharing, and has seen 75 cases of adoption across multiple disciplines and countries. It convenes various working and interest groups to develop solutions to challenges in areas like reference frameworks, data stewardship, and community needs.
The Research Data Alliance (RDA) aims to facilitate open sharing of data across technologies and disciplines to address societal challenges. There are two main components - the volunteer community that builds social and technical connections through Working Groups, and the business operations that support the community. Organizations performing research can engage with RDA in various ways like sponsorship, membership, or participation in Working Groups to help shape standards and address issues like data management, quality, and interoperability. RDA offers a global network and opportunities for collaboration on solutions to research data challenges.
The document discusses the value of libraries engaging with the Research Data Alliance (RDA). It outlines several benefits libraries can gain from involvement such as interacting with data professionals, developing strategic partnerships, and gaining expertise. Libraries are encouraged to become organizational members of RDA, have staff join working groups, adopt RDA recommendations, and send representatives to plenaries. RDA works to address challenges around research data reproducibility, preservation, best practices, and more through global collaboration. Libraries are positioned to augment RDA's network as bridges between data activities and open sharing.
The document discusses ways that research funders can engage with and benefit from the Research Data Alliance (RDA). RDA works to build infrastructure for open data sharing across disciplines. Funders that support RDA can get more value from the research they fund through improved data quality, reuse, and benefits to stakeholders. Funders can encourage adoption of RDA outputs, support RDA operations, participate in forums, and sponsor events, fellowships, and pilots implementing RDA recommendations. Engaging with RDA helps funders deliver more benefits from research and supports RDA's work of improving data sharing.
The Research Data Alliance (RDA) aims to build social and technical bridges to enable open sharing of data. It has over 8,800 members from 137 countries working in 87 groups to develop recommendations and standards to reduce barriers to data sharing. Some of RDA's outputs include recommendations on data citation, metadata standards, and repository interoperability.
The document discusses the value of the Research Data Alliance (RDA) for regions. It outlines how RDA supports regions in their work and business through various activities like disseminating regional efforts, facilitating connections, and providing organizational support. Regions also contribute value to RDA through participation in activities, hosting events, and providing financial support. The goal is to foster international collaboration to address challenges in sharing data across borders.
The Research Data Alliance (RDA) is an international organization with over 8,600 members from 137 countries that works to build social and technical bridges to enable open sharing of data. RDA has 104 working groups and interest groups that collaborate globally to develop recommendations and standards to reduce barriers to data sharing. Key activities of RDA include developing specifications, assessing community needs, and addressing challenges related to data citation, metadata, and interoperability.
The Research Data Alliance (RDA) is an international organization with over 8,400 members from 137 countries that works to build social and technical bridges to enable open sharing of data. RDA has 103 working groups and interest groups that collaborate globally to develop recommendations and outputs to reduce barriers to data sharing. Some of RDA's accomplishments include 32 flagship outputs, 75 adoption cases of their recommendations, and involvement of members from academia, public administration, and enterprise/industry.
The Research Data Alliance (RDA) is an international organization with over 8,200 members from 137 countries that works to build social and technical bridges to enable open sharing of data. RDA has 103 working groups and interest groups that collaborate globally to develop recommendations and outputs to reduce barriers to data sharing. Key activities of RDA groups include developing standards, addressing challenges in domains like agriculture and health, and ensuring data is reusable through practices like data citation.
The document discusses the Research Data Alliance (RDA) and its process for having its technical specifications identified and approved as ICT Technical Specifications by the European Commission. The RDA works with various stakeholders to develop technical specifications that enhance data sharing and interoperability. Its specifications undergo an open review process before being submitted for identification. Previously identified RDA specifications include recommendations for data citation, repository requirements, and workflows for data publishing. The identification process involves review by the European Multi Stakeholder Platform and the European Commission to ensure specifications meet requirements for adoption in European public procurement.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Removing Barriers to Data Sharing: the Research Data Alliance
1. Removing Barriers to Data Sharing:
the Research Data Alliance
Amy L. Nurnberger
0000-0002-5931-072X
RDA Organizational Advisory Board, Co-chair
Columbia University, Research Data Manager
@DataAtCU
Internet2 – 2016 TechEx
Miami, FL
28 September 2016
WWW.RD-ALLIANCE.ORG
@RESDATALL
This work is licensed under a Creative Commons Attribution 4.0 International License.
2. Research Data Driving Solutions to
Complex Scientific and Societal ChallengesWho is most at
risk to contract
asthma?
How can we increase
wheat yields?
How accurate is the
Standard Model of
Physics?
Image: Lucas
Taylor
How can we best
address energy
needs and
sustain the
environment?
Image: Ceinturion, Wikipedia
WWW.RD-ALLIANCE.ORG
@RESDATALL
4. World-wide Efforts Focusing on Infrastructure to
Support Research Data Sharing, Access, Use
Science, Humanities, Arts
Communities
E-Infrastructure professionals, data
analysts, data center staff, …
Data
Scientists
Libraries, Archives,
Repositories, Museums
WWW.RD-ALLIANCE.ORG
@RESDATALL
5. Institutional Data
Sharing Practice
Data Access and Distribution
Policy
Data
Discovery Tools
Common
Metadata Standards
Digital Object
Identifiers
Data Citation
Standards
Data
Analytics Algorithms
Data
Preservation Practice
Data Scientists and
Expert Support
Sustainable
Economic Models
Curation Practice and
Policy
Auditing, Certification and
Reporting Practice
Many Infrastructure Building Blocks
Needed to Accelerate Progress
Data Use and
Re-use
Data Discovery and
Data Sharing
Research Dissemination and
Reproducibility
Data Access (now) and
Preservation (later)
WWW.RD-ALLIANCE.ORG
@RESDATALL
8. … So much to gain from collaboration …
Why a Research Data
Alliance?
WWW.RD-ALLIANCE.ORG
@RESDATALL
9. “We are taking our work beyond Europe's borders, to reach
global scale. To make the scientific resources of the world work
together, interoperating and open to discovery. For example we
are working with partners like the US and Australia in the
Research Data Alliance to make scientific progress broader,
deeper and more workable”.
Neelie Kroes, Vice-President of the European Commission
responsible for the Digital Agenda - Open Access to science and data
= cash and economic bonanza, 19 November 2013
… So much to gain from collaboration …
Why a Research Data
Alliance?
WWW.RD-ALLIANCE.ORG
@RESDATALL
10. Researchers and innovators openly share data across
technologies, disciplines, and countries to address the grand
challenges of society.
What RDA is about:
WWW.RD-ALLIANCE.ORG
@RESDATALL
11. Researchers and innovators openly share data across
technologies, disciplines, and countries to address the grand
challenges of society.
… building the social and technical bridges that enable global open sharing of data…
Researchers, scientists, data practitioners & information technologists
from around the world are invited to work together to achieve the vision
What RDA is about:
WWW.RD-ALLIANCE.ORG
@RESDATALL
12. Reality bikes: “There is no reason to think that
collaborators have common goals”
Bicycle slide
Used courtesy Mark Parsons
WWW.RD-ALLIANCE.ORG
@RESDATALL
14. Dynamics of infrastructure
Edwards et al. 2007. Understanding infrastructure dynamics, tensions and
design
Infrastructures become “ubiquitous, accessible, reliable, and
transparent” as they mature
Systems → Networks → Inter-networks
◦ “system-building, characterized by the deliberate and successful
design of technology-based services”
◦ “technology transfer across domains and locations results in
variations on the original design, as well as the emergence of
competing systems.”
◦ Finally, a “process of consolidation characterized by gateways that
allows dissimilar systems to be liked into networks”
Used courtesy Mark Parsons
WWW.RD-ALLIANCE.ORG
@RESDATALL
15. ◦ Adopted code, policy, specifications,
standards, or practices that enable data
sharing
◦ Harvestable efforts for which 12-18 months
of work can eliminate a roadblock, or act as a
catalyst
◦ Efforts that have substantive applicability to
groups within the data community but may
not apply to all
◦ Efforts that can start today
Used courtesy Mark Parsons
Deliverables that make
data work
Create → Adopt → Use
RDA
Principles
• Openness
• Consensus
• Balance
• Harmonization
• Community
driven
• Non-profit
WWW.RD-ALLIANCE.ORG
@RESDATALL
16. Domain Science – focused: 4 WG & 13 IG
Community Needs – focused: 1 WG & 6 IG
Reference and Sharing – focused: 8 WG & 4 IG
Data Stewardship and Services – focused: 4 WG & 12 IG
Partnership Groups: 3 WG & 2 IG
RDA Interest (IG) & Working Groups (WG) by
Focus www.rd-alliance.org/groups
Base Infrastructure – focused
Array Database WG
Data Foundation and Terminology WG
Data Type Registries WG
Metadata Standards Catalog WG
Metadata Standards Directory WG
PID Information Types WG
Practical Policy WG
Data Fabric IG
Data Foundations and Terminology IG
Data in Context IG
Big Data IG
Brokering IG
Federated Identity Management IG
Metadata IG
PID IG
Service Management IG
Vocabulary Services IG
Total 73 groups: 27
Working Groups & 46
Interest Groups
WWW.RD-ALLIANCE.ORG
@RESDATALL
17. THE RDA OUTCOMES LEGEND
Recommendations: RDA’s equivalent of the “specifications”
or “standards” that other organisations create and endorse.
Supporting Outputs: are the outputs of RDA WGs and IGs
that are fruit of RDA work, but are not necessarily adoptable
bridges.
Other Outputs: include workshop reports, published
articles, survey results, etc. Anything a WG or IG wants to
register and report. Upon request, these are published and
discoverable on the RDA website but have no level of
endorsement.
https://rd-alliance.org/recommendations-and-outputs/
RDA Recommendations & Outputs
WWW.RD-ALLIANCE.ORG
@RESDATALL
18. Data Foundation & Terminology: a model for data in the
registered domain.
PID Information Types: a common protocol for providers
and users of persistent ID services worldwide.
Data Type Registries: allowing humans and machines to
act on unknown, but registered, data types.
Practical Policy: defining best practices of how to deal
with data automatically and in a documented way with
computer actionable policy.
https://rd-alliance.org/recommendations-and-outputs/all-recommendations-and-outputs
WWW.RD-ALLIANCE.ORG
@RESDATALL
RDA Recommendations & Outputs
19. Metadata standards directory: Community curated
standards catalogue for metadata interoperability
Data Citation: defining mechanisms to reliably cite
dynamic data
Data Description Registry Interoperability solutions
enabling cross platform discovery based on existing open
protocols and standards
Wheat Data Interoperability impacting the
discoverability, reusability and interoperability of wheat
data by building a common framework for describing,
representing linking and publishing wheat data
WWW.RD-ALLIANCE.ORG
@RESDATALL
https://rd-alliance.org/recommendations-and-outputs/all-recommendations-and-outputs
RDA Recommendations & Outputs
20. Repository Audit and Certification DSA–WDS: A convergent DSA-
WDS certification standard to help eliminate duplication of effort,
increase certification procedure coherence and compatibility thus
benefitting researchers, data managers, librarians and scientific
communities.
RDA/WDS Publishing Data Bibliometrics: improved research data
metrics and corresponding services, with the final goal of
increasing the overall availability and quality of citations and
research data itself.
RDA/WDS Publishing Data Services: A universal interlinking
service between data and the scientific literature.
RDA/WDS Publishing Data Workflows: enhance the possibilities
for greater discoverability and a more efficient and reliable reuse
of research data benefitting other stakeholders like publishers,
libraries and data centres.
https://rd-alliance.org/recommendations-and-outputs/all-recommendations-and-outputs
WWW.RD-ALLIANCE.ORG
@RESDATALL
RDA Recommendations & Outputs
21. Call for Adoption of RDA
Outputs
If you are interested in
adopting one of these
outputs, please contact
enquiries@rd-alliance.org
or visit https://rd-
alliance.org/recommendatio
ns-and-outcomes/become-
rda-adopter
WWW.RD-ALLIANCE.ORG
@RESDATALL
22. Africa
3% Asia
9%
Australasia
5%
Europe
48%
North America
34%
South America
1%
Total RDA Community Members: 4345
from 111 countries
Who is RDA?
Type
Members
(Sept. 2016)
Press & Media 27
Policy/Funding Agency 64
Large Enterprise 99
IT Consultancy/Development 143
Small and Medium Enterprise 249
Other 235
Government/Public Services 671
Academia/Research 2857
TOTAL 4345
www.rd-alliance.org/about-rda
392
989
1272
1654
2046
2402
2634
2877 3122 3431
3694
4016
4273
4345
May -
July
Aug -
Oct
Nov -
Jan
Feb -
Apr
May -
July
Aug -
Oct
Nov -
Jan
Feb -
Apr
May -
July
Aug -
Oct
Nov -
Jan
Feb-
Apr
May -
July
Aug -
Oct
WWW.RD-ALLIANCE.ORG
@RESDATALL
24. The value for Organizational
Members
Image, stature, and effectiveness among peers
• Recognized as developers and adopters of standards and
protocols
• Increased influence for their work on data
interoperability in their sectors, markets, and geographies
• Speaking with influence to national and international
funding agencies
• Member of world community that shares goals and
addresses common issues
WWW.RD-ALLIANCE.ORG
@RESDATALL
25. The value for Organizational
Members
Network effects
• Networking opportunity to share and promote best
practices and promote standard adoption when
appropriate
• Access to collegial consultancy resources when
developing a data management strategy for a new project
• Interacting with other Organisational Members (OMs) in
the OA sessions at RDA Plenaries
WWW.RD-ALLIANCE.ORG
@RESDATALL
26. The value for Organizational
Members
Organizational and technical interactions
• Having a voice inside RDA, providing advice on the needs
of their sectors and the problems faced in data exchange
• Ability to provide feedback on RDA activities, and suggest
future directions and next steps, by commenting on
group formations and outputs
• Providing advice to the RDA Council through the
Organisational Advisory Board
• Access on a regular basis to publication of RDA
Foundation budget and financial status
WWW.RD-ALLIANCE.ORG
@RESDATALL
30. https://rd-alliance.org/plenaries
RDA 10th Plenary Meeting
Montreal, Canada
Roger-Gaudry pavilion, Université de Montréal by Colocho, CC By SA 3.0, cropped
Montréal, Canada
Hosted by
With the support of Research Data
Canada
31. RemovingBarriersto
DataSharing:the
ResearchDataAlliance
WWW.RD-ALLIANCE.ORG/
@RESDATALL
RDA Global
Email - enquiries@rd-alliance.org
Web - www.rd-alliance.org
Twitter - @resdatall
LinkedIn -
www.linkedin.com/in/ResearchDataAlliance
Slideshare -
http://www.slideshare.net/ResearchDataAlliance
Facebook -
https://www.facebook.com/pages/Research-
Data-Alliance/459608890798924
RDA Europe
Email - info@europe.rd-alliance.org
Web - europe.rd-alliance.org
Twitter - @RDA_Europe
RDA US
Email - http://us.rd-alliance.org/contact-us
Web - us.rd-alliance.org
Twitter - @RDA_US
AmyL. Nurnberger
ANURNBERGER@COLUMBIA.EDU
Thanks to all thosein
RDAwho lent slides!
RDA IN A NUTSHELL
MARK PARSONS
JUAN BICARREGUI
32. What is RDA?
RDA is an international organization focused on the development of
infrastructure and community activities that reduce barriers to data
sharing and exchange, and the acceleration of data driven innovation
worldwide.
With more than 4,300 members globally representing more than 110
countries, RDA includes data science professionals from multiple
disciplines, including but not limited to academia, library sciences, earth
science, astronomy and meteorology.
RDA is building the social and technical bridges that enable open sharing of
data to achieve its vision of researchers and innovators openly sharing data
across technologies, disciplines, and countries to address the grand challenges
of society.
www.rd-alliance.org/about-rda
WWW.RD-ALLIANCE.ORG
@RESDATALL
33. What does RDA do?
Members come together through self-formed, volunteer, focussed Working
Groups, exploratory Interest Groups to exchange knowledge, share
discoveries, discuss barriers and potential solutions, explore and define
policies and test as well as harmonise standards to enhance and facilitate
global data sharing.
RDA members collaborate together regionally and with the global RDA
community to tackle numerous infrastructure challenges related to:
Reproducibility
Data preservation
Best practices for domain
repositories
Curriculum development
Data citation
Data type registries
Metadata
and so many more!
www.rd-alliance.org/about-rda
WWW.RD-ALLIANCE.ORG
@RESDATALL
34. Who Can Join RDA?
◦ Any individual or organization, regardless of profession or discipline,
with an interest in reducing the barriers to data sharing and exchange
and who agrees to RDA’s guiding principles of:
◦ Openness
◦ Consensus
◦ Balance
◦ Harmonization
◦ Community-driven
◦ Non-profit and technology-neutral
Membership is free @ http://www.rd-alliance.org/user/register
www.rd-alliance.org/about-rda
WWW.RD-ALLIANCE.ORG
@RESDATALL
35. Getting involved
Individuals
Observers
Contributors
Drivers
Organisations
Insight
Adopt
Drive
National level
Coordination & Knowledge
Exchange, Strategy & / or
Implementation
• Members
• WGs-IGs-BoFs
• Requests for
Comments
• Plenaries
• Member
• WGs-IGs-BoFs
• RfCs
• H2020 projects
• Adoption /
Uptake
• Papers & Events
• Meetings & Fora
• Training & Workshops
• Uptake pilots
www.rd-alliance.org/get-involved.html
WWW.RD-ALLIANCE.ORG
@RESDATALL
36. Why Join RDA?
Individual RDA Member Benefits
◦ Contribute to acceleration of data
infrastructure development
◦ Work and share experiences with
collaborators throughout the world
◦ Access to extraordinary network of
colleagues with various levels of
experience, perspectives and practices
◦ Gain greater expertise in data science
regardless of whether one is a student,
early or seasoned career professional
◦ Enhance the quality and effectiveness of
personal work and activities
◦ Improve one’s competitive advantage
professionally and positioning oneself for
leadership within the broader research
community
Organizational RDA Member
Benefits
◦ Provide an organizational perspective on the
work of RDA and ability to influence RDA’s
direction
◦ Assist in implementation of RDA Outputs
◦ Participate in all RDA Organizational Forums
◦ Receive regular updates on the work of the
RDA
◦ Attend Organizational Assembly meetings and
vote on proposed policies for consideration by
the RDA Council and for members of the
Organizational Advisory Board
◦ Provide advice to the Council through the
Organizational Advisory Board
◦ Be recognized on the RDA Website and at RDA
Meetings as a supporter of data
interoperability
www.rd-alliance.org/get-involved.html
WWW.RD-ALLIANCE.ORG
@RESDATALL
37. RDA Plenary 6 (Paris) – 23- 25
Sept 2015
RDA deliverables presented:
Repository Audit and Certification DSA–WDS
RDA/WDS Publishing Data Bibliometrics
RDA/WDS Publishing Data Services
RDA/WDS Publishing Data Workflows
7 Adoption cases: Deep Carbon Observatory, Platform for
Experimental Collaborative Ethnography, Datafed.net, the Materials
Innovation Infrastructure, EUDAT Collaborative Data Infrastructure,
German Climate Computing Center (DKRZ) & Common Language
Resources and Technology Infrastructure (CLARIN)
Focus on enterprise & climate change:
20 enterprises showcased solutions
3 climate change data challenge winners – Biovel, Plume Labs,
Vizonomy
Focus on emerging professionals :
RDA/EU sponsored 12 European Early Career Researchers and
Scientists & RDA/US sponsored 8 Fellowship winners
www.rd-alliance.org/plenaries/rda-sixth-plenary-meeting-paris-france
Theme: “Enterprise Engagement
with a focus on Climate Change”
700 attendees from 40+ countries
& hosted by Cap Digital – France
Co-located conferences:
1. eInfrastructures & RDA for Data
Intensive Science
2. Persistent Identifiers: Enabling
Services for Data Intensive Research
WWW.RD-ALLIANCE.ORG
@RESDATALL
38. 7 RDA Recommendations/outputs presented:
Repository Audit and Certification DSA–WDS
RDA/WDS Publishing Data Bibliometrics
RDA/WDS Publishing Data Services
RDA/WDS Publishing Data Workflows
Wheat Data Interoperability Recommendations
RDA/CODATA Summer Schools in Data Science and Cloud
Computing in the Developing World Interim Recommendations
Brokering Governance Interim Recommendations
11 adoption presentations
www.rd-alliance.org/plenaries/rda-seventh-plenary-meeting-tokyo-japan
• 30 international speakers over
5 plenary sessions
• 7 outputs & 11 adoption cases
• 8 Working Group meetings
• 25 Interest Group meetings
• 10 Birds of a Feather
• 9 Joint meetings
• 2 Organisational Member
meetings
• RDA for Newcomers Meeting
357 attendees
from 33
countries
WWW.RD-ALLIANCE.ORG
@RESDATALL
Editor's Notes
http://cms.web.cern.ch/news/about-higgs-boson
http://cms.web.cern.ch/news/about-higgs-boson
Because research is more and more about the data
Because research is more and more about the data
And sometimes it doesn’t work out
But we know what can happen if we don’t try
Guiding Principles:
Openness
Consensus
Balance
Harmonization
Community-driven
Non-profit and technology-neutral