Download to read offline
![data
Data, after all, is stuff machines can handle […]
we could create a world in which it would be programs
-- not just people -- that would enjoy the data.
For data, as for documents, the value of any part of the web is
increased by the amount of other stuff out there.
For documents it is the ability to follow links,
but for open data it is the ability to also interconnect and join,
to summarise and compare, to monitor, extrapolate, to infer.
Tim Berners-Lee, 2009
NOW!
• Provenance of data: STAR Methods at Cell
• Contributor Roles (CRediT) taxonomy
• Citation and linking to data and software
• Versioned linking to data & software
REAGENT/RESOURCE SOURCE IDENTIFIER
Antibodies
Rabbit monoclonal anti-
Snail
Cell Signaling
Technology
Cat#3879S; RRID:
AB_2255011
Mouse monoclonal anti-
Tubulin (clone DM1A)
Sigma-Aldrich Cat#T9026; RRID:
AB_477593
Rabbit polyclonal anti-
BMAL1
This paper N/A
Bacterial and Virus Strains
pAAV-hSyn-DIO-
hM3D(Gq)-mCherry
Krashes et al.,
2011
Addgene AAV5;
44361-AAV5
AAV5-EF1a-DIO-
hChR2(H134R)-EYFP
Hope Center Viral
Vectors Core
N/A
Cowpox virus Brighton
Red
BEI Resources NR-88
Zika-SMGC-1,
GENBANK: KX266255
Isolated from
patient (Wa 2016)
N/A
Staphylococcus aureus ATCC ATCC 29213
Streptococcus pyogenes:
M1 serotype strain: strain
SF370; M1 GAS
ATCC ATCC 700294
Biological Samples
Healthy adult BA9 brain
tissue
University of
Maryland Brain &
Tissue Bank
Cat#UMB1455](https://image.slidesharecdn.com/hpc20191120-191119214514/85/Why-would-a-publisher-care-about-open-data-3-320.jpg)
![32
Building the Corpus
19.11.2019
'papillomaviridae' AND 'cancer' AND [article]/lim - 2,747 results from 1975-2019
• 55,414 references total cited in this set
• 29,064 unique references (the references overlap) 1870-2019
• 719,470 references cited in this set of 29,064 papers
• 259,908 unique in this set.
Total corpus of work using this method is 182,402 unique articles
• Citation network has 103,443 edges](https://image.slidesharecdn.com/hpc20191120-191119214514/85/Why-would-a-publisher-care-about-open-data-32-320.jpg)
Uploaded byAnita de Waard
Why would a publisher care about open data?
A publisher would care about open data for several reasons: 1) Open data increases the value of all parts of the web by allowing programs, not just people, to utilize the data through interconnecting and joining it. 2) Publishers are evolving from linear supply chains focused on content delivery to users, to becoming marketplaces that optimize the number of interactions between users through networked open science. 3) The future of publishing involves networked open science where data is openly accessible, annotated with metadata, and linked together in research objects, increasing findability, accessibility, interoperability, and reusability of research outputs.
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Why would a publisher care about open data?
- 1. Why would apublisher care about open data? Anita de Waard November 2019
- 2. Why would apublisher care about open data? What do we mean by open? What do we mean by data? What do we mean by a publisher?
- 3. data Data, after all,is stuff machines can handle […] we could create a world in which it would be programs -- not just people -- that would enjoy the data. For data, as for documents, the value of any part of the web is increased by the amount of other stuff out there. For documents it is the ability to follow links, but for open data it is the ability to also interconnect and join, to summarise and compare, to monitor, extrapolate, to infer. Tim Berners-Lee, 2009 NOW! • Provenance of data: STAR Methods at Cell • Contributor Roles (CRediT) taxonomy • Citation and linking to data and software • Versioned linking to data & software REAGENT/RESOURCE SOURCE IDENTIFIER Antibodies Rabbit monoclonal anti- Snail Cell Signaling Technology Cat#3879S; RRID: AB_2255011 Mouse monoclonal anti- Tubulin (clone DM1A) Sigma-Aldrich Cat#T9026; RRID: AB_477593 Rabbit polyclonal anti- BMAL1 This paper N/A Bacterial and Virus Strains pAAV-hSyn-DIO- hM3D(Gq)-mCherry Krashes et al., 2011 Addgene AAV5; 44361-AAV5 AAV5-EF1a-DIO- hChR2(H134R)-EYFP Hope Center Viral Vectors Core N/A Cowpox virus Brighton Red BEI Resources NR-88 Zika-SMGC-1, GENBANK: KX266255 Isolated from patient (Wa 2016) N/A Staphylococcus aureus ATCC ATCC 29213 Streptococcus pyogenes: M1 serotype strain: strain SF370; M1 GAS ATCC ATCC 700294 Biological Samples Healthy adult BA9 brain tissue University of Maryland Brain & Tissue Bank Cat#UMB1455
- 4. 19.11.2019 Elsevier Data Solutionsfor Research open Scholix: A Linked Open Data Hub to connect papers and datasets Research Object Composer: An Open source editor for Research Objects
- 5. a publisher What doesa publisher even do anymore? cites 20081977 newexisting Example 1: Human papilloma virus causes cervical cancer
- 6. What does apublisher even do anymore? Example 2: Top 20 universities in Quantum Computing
- 7. 7 7 Author Editor/ Publishers Reader/ User Researcher Data Results ArticleUI article article article article tool tool data user user tool data article article tool tool data data data datauser user user article Model: Castle • Goal: selling content • Metrics: number of units sold • Strategy: optimize content delivery to users Model: Marketplace • Goal: grow number of interactions • Metrics: number of interactions between users • Strategy: optimize number of network interactions Today: linear supply chains Linear supply chains are evolving into complex, dynamic and connected value webs Win by reputation Win by trust Why publishers care about open science: The future: networked open science
- 8. 19.11.2019 Elsevier Data Solutionsfor Research Extra Slides: 1. Elsevier in numbers 2. Research Data Management 3. Research Object Composer 4. Entellect and Life Science Solutions 5. Data analytics: Quantum Computing 6. Elsevier and Open Science
- 9. 19.11.2019 Elsevier Data Solutionsfor Research 1. Elsevier by the numbers
- 10. Elsevier by thenumbers 25,000 Our products are used at more than 25,000 Academic and Government institutes globally 14+ m people a month use Science Direct, our flagship platform for academic research 320+ Reaxys®'s ML capability enables the chemistry of drug discovery, and materials innnovation for over 320 pharma innovators, 130 chemical companies, and over 1100 7,500 Elsevier has 7,500 employees and serves customers in over 180 countries. 430,000 Elsevier publishes 430,000 peer-reviewed articles annually 9 m Mendeley is a scientific social media platform that enables around 9 million users worldwide, to organize, write, collaborate and promote their
- 11. 19.11.2019 Elsevier Data Solutionsfor Research 2. Research Data Management
- 12. 19.11.2019 Elsevier Data Solutionsfor Research Elsevier Data Solutions for Research DisseminateAnalyzeCollaborateControlStoreCreate & Collect Collect Create Extract Store Secure Manage Control Workspaces Researchers Data sets Search Integrate Analyze Share Publish Archive EntellectTM MACRO EDC Hivebench GDPR
- 13. 19.11.2019 Elsevier Data Solutionsfor Research How we deliver 1. Open system: through open APIs, modules can be integrations with other RDM tools 2. Data remains private at or owned by institution 3. System is integrated with the researcher workflows, to ensure simple and clear use 4. Researchers continue to work the same way, avoiding additional bureaucracy and administration
- 14. 19.11.2019 Elsevier Data Solutionsfor Research Data Search Retrieve active data, discover public data Discover data • 10 million+ datasets indexed from more than 35 repositories • Deep indexing of data significantly enhances the relevancy of results • Keyword search within data files • Filter search results by specific author, institution, journal, subject category Retrieve active data* • Navigate to locally held institutional data • Powerful keyword search and filtering
- 15. 19.11.2019 Elsevier Data Solutionsfor Research Data Manager Researchers can • Share data privately within a research project • Invite external collaborators to join a project • Gather research data from data sources as it’s generated (including ELNs) • Annotate research data with detailed, subject- specific metadata • Curate data according to project or institutional workflows • Prepare to publish data on a repository of your choice • Open APIs allow tailored upload forms, automated workflows, analyze and re-upload data files Go from raw files to active datasets
- 16. 19.11.2019 Elsevier Data Solutionsfor Research Data Repository Researchers can • Store up to 100 GB of data per dataset in many formats • Describe how experiments can be reproduced • Keep track of dataset versions • Create DOI for citation (or university prefix) Store datasets in a secure and trusted repository
- 17. 19.11.2019 Elsevier Data Solutionsfor Research Data Monitor Institutions can • Keep track of data inside and outside institution • Achieve credibility, visibility and integrity of key research outputs • Maintain visibility of events in RDM space • Improve researcher's adoption of data sharing tools • Communicate value of data sharing to researchers during the research process Encourage and monitor compliance
- 18. Five Facts aboutElsevier and Research Data Fact #1 Elsevier’s Mendeley Data supports the entire lifecycle of research data The 5 modules that make up Mendeley Data are specifically designed to utilize data to its fullest potential, simplifying and enhancing current way of working. Fact #3 Mendeley Data is an open system It is a flexible platform — modules are designed to be used together, standalone, or combined with other Elsevier and non-Elsevier solutions Fact #2 Researchers and institutions own and control all the data Mendeley Data allows researchers to keep data private, or publish it under one of 16 open data licenses, so they stay in full control Fact #4 Mendeley Data can increase the exposure and impact of research Mendeley Data Search indexes over 10 million datasets from more than 35 repositories Fact #5 Elsevier is an active participant in the open data community Elsevier partners with the open data community, and is currently working on more than 20 projects globally
- 19. 19.11.2019 Elsevier Data Solutionsfor Research Mendeley Data already integrates through open APIs with the global Research Data Management ecosystem, as well as other Elsevier solutions + 35 repositories (BePress planned) • Mendeley Data Repository datasets are automatically synced with the Pure curation workflow • Projects, grants, equipment, showcase on portal (planned) • Mendeley Data Search results are visible on Scopus • Notify new articles to Monitor for data sharing compliance • Datasets appear as records on Scopus (planned) • Mendeley Data usage is accessible through Plum API and widget • Plumx metrics (citations, usage, social mentions) are captured and shown on Mendeley Data Repository Publish datasets alongside an article on Mendeley Data within the SSRN publication flow Publish or link datasets alongside an article on Mendeley Data within the ScienceDirect publication flow Researcher and Institutional Dataset metrics • User identity & login • Library (planned) • Notes (planned) • Projects (planned) Existing integration Planned integration • Mendeley Data indexed by OpenAIRE index • OpenAire Zenodo repository indexed by Mendeley Data Search Long-term preservation of published datasets Links between articles and datasets: • Contributed by Mendeley Data to Scholix • Indexed by Menndeley Data Search and Data Monitor • Consumed by Scopus and ScienceDirect Integrate with machine readabledata management plans • For more than 35 repositories the metadata as well as the underlying datasets are indexed by Mendeley Data Search • First repositories are actively integrating with the free and open ‘push API’ of Mendeley Data Search • Mint DOIs for Mendeley Data Repository • Data Cite indexed by Mendeley Data Search
- 20. 19.11.2019 Elsevier Data Solutionsfor Research 3. Research Object Composer
- 21. Building an openinteroperable data ecosystem: Aggregates link things together Annotations about things & their relationships Container Packaging content & links: Zip files, BagIt, Docker images Identification locate things regardless where 21
- 22. Building an openinteroperable data ecosystem: database Open repository Workflow Tool Task 1 Workflow Input Task 2 Task 3 Output Research Object Composer http://www.researchobject.org Research Object Profiler Add annotation and relationships (metadata) to collection to describe a research object: - URI - Length - Filename - Checksums etc. Research Object Serializer (a manifest itemizing file names) Serialise Research Object in standard format based BagIt =1 =2 =3 RO 1 2 3 Open API 22 Mendeley Data RO 1 2 3 • DOIs • Metadata (Findability) • Open repo (Accessibility) • Versioning • RO Standard (Interoperability, Reusability)
- 23. • The ROComposer is not a registry of research objects, but it can list research objects currently under construction. • The RO Composer is a microservice which responsibility is to help other services create and deposit research objects. • The composer acts as a temporary construction site that can be completed by multiple services (e.g. a data management system, a workflow system, a user interface). • These clients will be jointly building a Research Object that can then be validated according to the schema, before the RO is downloaded or deposited into an archive (like Zenodo or Mendeley Data). • Clients of the RO Composer are applications (driven by a user interface) or agents (engaged automatically from other events, e.g. a workflow run). • The RO Composer is not a required component to this: any software may generate research objects by following Research Object specifications. Purpose of the Research Object Composer*: 23* From: https://github.com/ResearchObject/research-object-composer/blob/master/introduction.ipynb
- 24. • API: https://researchobject.github.io/research-object-composer/api/ •Source: https://github.com/ResearchObject/research-object-composer • Link to Jupyter Notebook tutorial (even I can do it!) You can drive it today! 24
- 25. 19.11.2019 Elsevier Data Solutionsfor Research 4. Entellect and Life Science Solutions
- 26. 19.11.2019 Elsevier Data Solutionsfor Research
- 27. 27 Human Papilloma Virusand Cervical Cancer 2008 zur Hausen awarded Nobel Prize 1976 zur Hausen proposes link between HPV and Cervical Cancer 1946 Papanicolau develops PAP smear 2006 Gardasil HPV vaccine approved Study impact of intervening research in this talk
- 28. 28 Early Work 1977 “a hypothesishas been presented that the virus found in genital warts may be involved in the etiology of human genital cancer”
- 29.
- 30. 30 Citation Mapping Process 19.11.2019 Buildcorpus of papers using broad search (~20,000 papers) on all aspects of cervical cancer and HPV Expand corpus by adding all cited works not in the original corpus Add cited works from the cited corpus (“grandchild” references ) Connect the discrete steps of scientific advances connecting the works Apply graph mathematics to find all connected paths
- 31. 31 Assembling The Graph 19.11.2019 •Dense interconnected web of cititations • Filter for only cited works within 3 years of the citing work – building on relevant knowledge First level Second level Recognize identities in graph Corpus
- 32. 32 Building the Corpus 19.11.2019 'papillomaviridae'AND 'cancer' AND [article]/lim - 2,747 results from 1975-2019 • 55,414 references total cited in this set • 29,064 unique references (the references overlap) 1870-2019 • 719,470 references cited in this set of 29,064 papers • 259,908 unique in this set. Total corpus of work using this method is 182,402 unique articles • Citation network has 103,443 edges
- 33. 33 Path Finding 19.11.2019 Select “interesting”endpoints • Significant starting point – proposal that HPV could be related to cancer • Significant endpoint – recognition of HPV/cancer connection Use graph traversal analytics to find all paths greater than 5 papers that connect the two ideas Separate by year
- 34. 34 Example Pathway LinkingIdea to Vaccine 17 links. 30 years. 19.11.2019
- 35. 35 Resulting Graph 19.11.2019 Represents the incrementaladvances by year from concept to acceptance 20081977 New cites existing
- 36. 19.11.2019 Elsevier Data Solutionsfor Research 5. Data Analytics: Quantum Computing
- 41. Quantum Computing Research:Highest FWCI Non-US
- 42. Quantum Computing Research:Highest FWCI US
- 43. Quantum Computing ResearchWorldwide--FWCI
- 44. Top 20 universitiesactive in Quantum Computing University of Waterloo National University of Singapore Massachusetts Institute of TechnologyUniversity of Science and Technology of China University of Oxford Tsinghua University University of Tokyo Harvard University University of Maryland University of New South Wales University of California at Santa Barbara ETH Zurich University of Sydney RAS University of Southern California Perimeter Institute for Theoretical Physics University College London Princeton University University of Michigan 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 0 50 100 150 200 250 FWCI Publications
- 45. Quantum Computing Researchin Top 10% Citation Percentile, US vs. Non-US
- 46. Quantum Computing ResearchWorldwide--Academic-Corporate Collaboration
- 47. Academic-Corporate Collaboration—Network Mapof Top 20 universities in Quantum Computing
- 48. Quantum Computing: Academic-CorporateCollaboration and Patent Citations per Scholarly Output, US vs. Non-US
- 49. Quantum Optics/Flux Qubits:Academic-Corporate Collaboration and Patent Citations per Scholarly Output, US vs. Non-US
- 50. Quantum Optics/Flux QubitsResearch by Country
- 51. Quantum Optics/Flux Qubits—TopCorporates
- 52. Quantum Optics/Flux Qubits—TopUniversities
- 53.
- 54. 19.11.2019 Elsevier Data Solutionsfor Research 6. Elsevier and Open Science
- 55. ELSEVIER I ElsevierOpen Science: Creating value through collaboration I CONFIDENTIAL 55 Global market dynamics and technologies are reconfiguring the academic ecosystem: Macroeconomic developments Ecological and societal sustainability • Global population is growing; 9B people in 2050 • Challenge to produce more with less and cleaner input • Challenge to solve poverty and unequal allocation of resources Shifting power balance from West to East • Strong economic growth in China and India • Rise of the middle class; improvement of educational and health care system and food supply chain Technological developments The web • Everyone is a publisher • Content access is ubiquitous The social web • Professional and personal networks emerge without traditional institutions • Everyone is a peer reviewer Big data • Explosion of data through networking of measurement tools • Radically cheaper tools and computing power Social developments • Pressure from society and funders to justify the costs of science • Need for reliable research results (that can be trusted). • Patients/citizens demand access. increased participation • Distributed computing makes it easier to make and share tools, content and code • Overall need for more transparency and accountability, also in doing and reporting research Emergence of open science Open Peer Review New social networks Data, tools and workflows are sharedOpen Data Society is engaging moreOpen API’s Open Source Software
- 56. ELSEVIER I ElsevierOpen Science: Creating value through collaboration I CONFIDENTIAL Carl Kesselman builds tools to enable neuroscientists to store and share their data in a better way Viktor Pankratius builds software programs that generate hypotheses about volcano eruptions: the software can steer drones to collect data. Lena Deus solves scientific problems through Kraggle: the system awards her points for scoring highest on Machine Learning tasks. Scientists build data sharing tools Computers are scientists Science becomes a game, which anyone can play Some examples of Open Science:
- 57. ELSEVIER I ElsevierOpen Science: Creating value through collaboration I CONFIDENTIAL 57 Moving to a network of connected components: Take an Open Source data repository and find some Open Data:1 Deriva, an Open Source data repository 2 Write some Open Source software to mash them up: 3 Share outputs as OA/OD/OS: Share new data sets on data Deriva Publish papers in an OA journal Share code on platforms like Github user A 1 Community adds elements to open science platforms that can be used by everyone. 2 Researchers build upon the combination of shared content/system elements. This leads to new scientific knowledge and output. All sharable elements find their way to other open platforms and formats and can be re-used, causing a network effect. 3 Networked system: PLATFOR M A Data v1 user B PLATFOR M BTools B Open Research Platform Data v2 Tools Carticle user C Open Data Repositorie s Open Access Journals Code Networks Neuroscience data Jupyter Notebook to calculate properties Share code on platforms like Github
- 58. ELSEVIER I ElsevierOpen Science: Creating value through collaboration I CONFIDENTIAL 58 Manu- facturers Distri- butors Consu- mers Suppliers data tool article user article article article article tool tool data user user tool data article article tool tool data data data datauser user user article Open Science represents a transition from a pipeline to a networked knowledge system: Model: Castle • Goal: selling content • Metrics: number of units sold • Strategy: optimize content delivery to users Model: Marketplace • Goal: grow number of interactions • Metrics: number of interactions between users • Strategy: optimize number of network interactions Today: linear supply chains The future: networked open science Linear supply chains are evolving into complex, dynamic and connected value webs Win by reputation Win by trust
- 59. ELSEVIER I ElsevierOpen Science: Creating value through collaboration I CONFIDENTIAL 59 Some current Open Science efforts: Open Access Open Data Open Metrics Research Integrity & Reproduci bility Science & Society Open Tools and Software Open Science Open Access: - Hybrid/Gold journals, open/self- archive options - Contributing to CHORUS, CrossMark, RA21 - ‘Platinum OA’ on bepress Digital Commons - Pilot SSRN Preprint of the Lancet . Research Integrity and Reproducibility: Many efforts, including: - Full GDPR Compliance across all Elsevier products - Preregistration and Registered Reports - STAR Methods for Cell, transparent reporting - Plagiarism and Image manipulation detection - Statistics checking - Reproducibility badges/TOP guidelines - Transparency in contributorship roles (CRediT Taxonomy) - Research collaborations e.g Humboldt, Data Integrity Science and Society: - Science Literacy effort: Topic Pages, Audioslides, Science and People - Access to content via Patient Inform, Research4life, Bookshare and Load2Learn. - Elsevier Foundation supporting many projects including Green and Sustainable Chemistry, awards for early-career women scientists from developing world, many more Open Data: - All data is open on all platforms - Following TOP guidelines across board - Coleads on Enabling FAIR Data requiring data deposits in Earth & Space Science - Coleads Data Citation Principles in Force11 - Supporting Scholix Linked Data repository and other open data standards, efforts through RDA, ORCID, CrossRef, etc Open Metrics: - CiteScore free API - PlumX metrics and NewsFlo: free layer of societal impact metrics on article level - Helping lead RDA Make Data Count effort with CDL/Datacite to establish data metrics Open Tools and Software: - Open APIs for most products - Many research collaborations leading to Open Source software, e.g. Github4Labs, NIH Data commons - Hackathons, in medicine <Elsevier Hacks>, for Mendeley - Content and data available for research and development and hackathons
Editor's Notes
- #16 Analogies: Manager is like OneDrive for dataset: collaborate on active project; Allows for review and approval of datasets prior to publication by library Manager is the Trello for research project management RESEARCHER: Example from Wouter: Why would a psychologist use this? Project management dashboard : It enables organized project management (where is the data? Could be dropbox) Templates can be set up MOVE FROM FILES TO DATASET (files with description, metadata and structure) Manager helps make your data FAIR INSTITUTION: Monitir allows for clear presentation and enables librarians to make a decision to keep/delete private data, esp when someone has left the instituions. Archival policies. Monitor helps prevent «data loss»
- #17 Now let’s dive a little deeper into each module, starting with Repository. We know that counting only publications does not reflect the true amount of research created during an experience- we know there is likely more than 1 dataset tied to a published article. By using Repository, Researchers can: Store up to 100GB of data per dataset Ensure proper metadata tagging and storage Increase discoverability of their dataset by easily creating a DOI to allow for citation. This also ensures datasets gets counted as a research output.
- #22 Standards-based metadata framework for logically and physically bundling resources with context http://researchobject.org
- #38 So let’s get to quantum computing, which is the area we were asked to focus on within the larger topic of quantum technologies. Here we can see the institutions that create the largest number of papers on QC, with the Chinese Academy of Sciences and CNRS, two national lab systems, at or near the top.
- #39 If we flip this to look at field-weighted citation impact, however, a measure of the works relative impact in the field, we get a very different picture—still highly international, but more US institutions here, and notably a number of US companies producing high-impact work.
- #40 The word cloud represents the top 50 semantically-derived keyphrases for the total set of papers representing quantum computing.
- #41 If we click on the specific term “polynomial approximation” in the word cloud, we find out how quickly the topic is growing over the last 5 years, and even which individuals and instituions worldwide are working on the particular concept of polynomial approximation. It’s immediately evident that quantum computing is a highly international and competitive field. And remember, 50 keyphrases exist for each of the 100,000 topics that are modeled in the topic prominence calculation.
- #42 Let’s slice the data in a different way. Here are top 20 institutions outside the US, again arranged by FWCI, who are doing important work in quantum computing. Notice anything? Virtually every one of these is a university.
- #43 Here’s the same list for the US. What is different here? For the US list alone, there are 3 large corporations, the NSF, and a DOE national lab contributing high-impact research. We know that quantum computing is being invested in and chased vigorously across the globe. The Chinese are pouring immense financial resources into this, and they have plenty of human talent, including many who are likely employed by the people in this room. In my view, it is this nexus of different organizations, the close linkages between them, that gives the US its edge, if we have any edge. SEMATECH is another example of a complex of different organizations engaging in coordinated action. Over 90% of the research papers that Google publishes, and over 80% that IBM publishes, are done with one or more collaborators from academia.
- #44 So what does this difference look like in action? This geomap captures global research activity in quantum computing. The size of the bubble is the number of papers, the color intensity is the FWCI. Here we can see research is fairly evenly distributed in the US, Europe, and East Asia.
- #45 The Y axis here is the Field-Weighted Citation Impact for each university, while the position on the X-axis looks at total number of papers—clearly UC Santa Barbara is doing something exceptional here, we’ll explore that a bit more later. Waterloo and NUS are producing a lot of papers, though at a relatitvely low citation impact. Generally, the more papers one is publishing, the lower the overall impact will be. (traken from slightly different data set)
- #46 We can look at other proxies for quality, including the number of outputs in top percentiles—here the percentage of research in the top 10% of cited outputs, which is around 29% for the US in 2016, around 15.5% for non-US institutions.
- #47 Here’s the same map, but now the color intensity is the level of academic-corporate collaboration. The dark red are tech companies, but US universities also have much higher levels of AC collaboration than others. Europe and Asia are very pale by comparison.
- #48 Let’s look at different and more granular view of the same information. So there is a lot going on in this graphic—It’s a different way of looking at the landscape. The bluer the dot, the higher the FWCI. The thicker the line, the more papers are shared between between the two nodes. Network centrality implies higher levels of connectedness. Japan is peripheral and mostly connected to other Japanese entities. China, particularly Tsinghua and UST China, are more connected, Singapore still more so. However, they are not as connected or central as a few key US, UK, Australian and Canadian institutions, and one can clearly see that as few large US corporations are also quite central here. In my view, one remaining advantage the US seems to have (in addition to lots of high-quality research) is the nexus between industry and academia--because of the enormous manufacturing complexities, the SEMATECH kind of highly coordinated approach (academia/industry/govt) may make more sense in this sector than many--also given questions of cryptographic security and national security implications.
- #49 ,. We can also look at three-factor analysis. Here we map total scholarly output on the Y axis. US output of 2392 papers (2008-2016) represents about 27% of global output. The X axis is the level of academic-corporate collaboration. 7.7% of US papers, but only 1.2% of non-US papers, are AC collaborations. Finally, the size of the bubble shows the number of patent citations for every thousand papers published. For the US, this is 111 citations, meaning over 11% of these papers were cited in patents worldwide. It generally takes 3-5 years before papers are cited in patents, so this likely understates the total since we have 2016 papers in here. The same measure for non-US institutions is 21.6 per 1000, less than one-fifth the level. This graphic really points out the large gap between the US and the ROW regarding UI collaboration, and overall patenting activity driven by university research as well.
- #50 The quantum computing topic is actually an aggregate made up of somewhat more and distinct granular topics—the same kinds of analysis can be done on these topics, which are generated directly from the topical model that I covered earlier.
- #51 This is the same topics by country and number of downloaded articles.
- #52 We can look at top corporations publishing in this area, and can see that the bulk are US firms with some Japanese representation as well.
- #53 Top universities for the same topic—Yale, UCSB, Berkeley, and MIT produce a great deal, with UCSB and Yale authors having a particularly high FWCI
- #54 One can always do a Keyphrase-based analysis if you want to delve into a particular aspect of the topic. Here we look at the same set of papers on flux qubits that cover the concepts of circuits, resonators, and Josephson junctions—note the number of papers from Yale has gone down from 85 to 46 here. Dr. Devoret has produced more work than anyone else covering these concepts.