Digital Science

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An overview of Digital Science - a new company started out of Macmillan Publishers dedicated to making research more efficient through better use of technology.

Technology

kaitlin thaney
mgr, external partnerships

technology company
publisher link
london, nyc, tokyo

research
idea

publish lit review

share results materials

retest
analyze experiment

collect data

blocking points
(to name a few ... )
idea

publish lit review

share results materials

retest
analyze experiment

collect data

discovery &
delivery
content
data
materials

changes at the
workbench
knowledge exchange
materials, machines
ordering
automation

annotation &
curation
machine-readable
ontologies
metadata

social &
administrative
P&T systems / funding
reputation
policy making
incentives / impact factor

remaining
roadblocks
specialisation of tools (+/-)
interoperability
accessibility
design decisions
the “social issue”

efficient search
discoverability
information management
analysis
incentives

knowledge discovery
software applications
research management

steps towards better
annotation and search

10,11-dihydro-5-methyl-5H-dibenzo[b,e][1,4]diazepin-11-one

CC-BY-2.0 - Plaxco Lab - http://www.flickr.com/photos/34857812@N04/

“Right now we're going through a
Cambrian explosion of metrics.”
- Johan Bollen

Nature 465, 864-866 (2010) | doi:10.1038/465864a

citation / impact factor
h - index
weighted citations (eigenfactor, sjr)
“betweenness centrality”
alt-metrics, etc.

difficult to ...
harmonise
track /maintain
reward
(even still measure)

investment arm
incubator role
in-house dev

tiered approach
build to scale
researcher-focused

RAE teams pharma
startups
tools developers
content providers

partners/collaborators
big business
funding agencies
researchers
grant officers administrators

RAE teams pharma
startups
tools developers
content providers

work with us
big business
funding agencies
researchers
grant officers administrators

more efficient research
increase productivity
better science

thank you.

k.thaney@digital-science.com
www.digital-science.com

The document discusses the "mudslide hypothesis" which suggests that traditions persist not due to excellence but because of resistance to change from influential people and the difficulties of transition. It argues that outdated research practices waste time and resources, and that while research is changing, discovery is still suboptimal due to reliance on old systems not designed for modern mediums. It calls for rethinking approaches to research to maximize reuse, allow for network effects, and redefine performance metrics to better support current research workflows.

Making the web work for science - RIT Dean's Lecture Series

Kaitlin Thaney

The document discusses challenges with the current state of scientific research and proposes ways to leverage the power of the open web to improve science. It notes that current systems are designed to create friction rather than enable open collaboration. The document advocates for adopting practices of open source development like using community-driven metadata for software and open, iterative development. It also argues that policies and incentives need to change to reward openness, reuse and reproducibility in order to avoid wasted time, money and opportunities.

Knowledge Sharing - aCCCeso

Kaitlin Thaney

Building capacity for open, data-driven science - Grand Rounds

Kaitlin Thaney

Kaitlin Thaney gave a presentation on building capacity for open, data-driven science. She discussed leveraging the power of the web for open scholarship through access to content, data, code and materials. Adopting practices from open source development like code as a research object and iterative development can help further open science. Building capacity requires fostering sustainable practitioner communities through rewards, incentives and reputation systems while providing professional development support and lowering barriers to entry. Shifting to open practices is challenging and requires tools, cultural awareness, connections, skills training and incentives.

Leveraging the power of the web - Open Repositories 2015

Kaitlin Thaney

This document discusses leveraging the power of the open web for science. It notes that current systems are creating friction despite original intentions of openness. It advocates for building capacity for open, web-enabled research through infrastructure, tools, standards, incentives and training to support reuse, collaboration and interoperability. The goal is to foster sustainable communities of practitioners doing open science.

Leveraging the power of the web - Rocky Mountain Advanced Computing Conference

Kaitlin Thaney

This document discusses leveraging the power of the open web for science. It argues that current systems are creating friction despite original intentions of openness. It advocates for open tools, standards, best practices, and incentives to support web-enabled open research through improved access to content, data, code, materials. This would allow for communication, reuse, and scaling in a distributed environment. It also discusses fostering open source development communities of practice and building capacity for open research through professional development, training, and rewards.

Big Data's Long Tail

John Kunze

Big data repositories are seeing an increase in smaller, niche datasets as more researchers contribute data. This "long tail of data" poses challenges for discovery, access, and attribution. The authors propose a centralized data repository that would make any dataset discoverable and accessible regardless of size or topic by automating metadata generation and attribution to help researchers find and share relevant data.

Scott Edmunds A*STAR open access workshop: how licensing can change the way w...

GigaScience, BGI Hong Kong

Keynote for Theory and Practice of Digital Libraries 2017 The theory and practice of digital libraries provides a long history of thought around how to manage knowledge ranging from collection development, to cataloging and resource description. These tools were all designed to make knowledge findable and accessible to people. Even technical progress in information retrieval and question answering are all targeted to helping answer a human’s information need. However, increasingly demand is for data. Data that is needed not for people’s consumption but to drive machines. As an example of this demand, there has been explosive growth in job openings for Data Engineers – professionals who prepare data for machine consumption. In this talk, I overview the information needs of machine intelligence and ask the question: Are our knowledge management techniques applicable for serving this new consumer?

Nicole Nogoy: GigaScience...how licensing can change the way we do research

GigaScience, BGI Hong Kong

Bionimbus: Towards One Million Genomes (XLDB 2012 Lecture)

Robert Grossman

The document discusses lessons learned from Bionimbus, a petabyte-scale science cloud service provider. Some key points: - Bionimbus provides cloud-based storage and computing resources for genomic and biomedical research projects dealing with large datasets, such as sequencing a million genomes which would generate around 1 exabyte of data. - Lessons from operating at this scale include how to effectively store, manage, analyze, and share extremely large datasets across many users and projects in an open science environment. - The growth of "big data" sciences like genomics poses challenges around data volume but also opportunities to drive new scientific discoveries through large-scale genomic analysis.

A First Step Towards Content Protecting Plagiarism Detection

Scientific Information Analytics Group, Prof. Gipp

Enabling knowledge management in the Agronomic Domain

Pierre Larmande

Contractor-Borner-SNA-SAC

webuploader

The document discusses various challenges in social network analysis including collecting and extracting network data at scale from sources such as the web, validating automated data extraction methods, and developing algorithms and software that can analyze large and complex network datasets. It also outlines different network analysis methods, visualization and simulation techniques, and recommendations for how tools can better support networking, referrals, and workflows across multiple data sources and programs. Scaling methods and algorithms to very large network sizes and developing standards to integrate diverse data and tools are highlighted as key challenges.

Making the web work for science - eResearch nz

Kaitlin Thaney

1) The document discusses how current scientific practices are outdated and designed to create friction, limiting access to information and collaboration. 2) It argues that leveraging the power of the open web through tools, data sharing, and interoperability could help advance science by improving access, reuse of resources, and transparency. 3) However, changing practices and building an open, collaborative culture also requires training researchers in digital skills and establishing social infrastructure to encourage openness and reward sharing.

The need for a transparent data supply chain

Paul Groth

1. The document discusses the need for transparency in data supply chains. It notes that data goes through multiple steps as it is collected, modeled, and applied in applications. 2. It illustrates the complexity of data supply chains using examples of how data is reused and integrated from multiple sources to build models and how bias can propagate. 3. The document argues that transparency is important to understand where data comes from, how it has been processed, and help address issues like bias, privacy, or other problems at their source in the data supply chain.

Sci Know Mine 2013: What can we learn from topic modeling on 350M academic do...

William Gunn

Uc3 pasig-asis&t-2013-08-20-support-of-data-intensive-research

University of California Curation Center

Information technology and resources are an integral and indispensable part of the contemporary academic enterprise. In particular, technological advances have nurtured a new paradigm of data-intensive research. However, far too much of this activity still takes place in silos, to the detriment of open scholarly inquiry, integrity, and advancement. To counteract this tendency, the University of California Curation Center (UC3) has been developing and deploying a comprehensive suite of curation services that facilitate widespread data management, preservation, publication, sharing, and reuse. Through these services UC3 is engaging with new communities of use: in addition to its traditional stakeholders in cultural heritage memory organizations, e.g., libraries, museums, and archives, the UC3 service suite is now attracting significant adoption by research projects, laboratories, and individual faculty researchers. This webinar will present an introduction to five specific services – DMPTool, DataUp, EZID, Merritt, Web Archiving Service (WAS) – applicable to data curation throughout the scholarly lifecycle, two recent initiatives in collaboration with UC campuses, UC Berkeley Research Hub and UC San Francisco DataShare, and the ways in which they encourage and promote new communities of practice and greater transparency in scholarly research.

FAIRy stories: the FAIR Data principles in theory and in practice

Carole Goble

https://ucsb.zoom.us/meeting/register/tZYod-ippz4pHtaJ0d3ERPIFy2QIvKqjwpXR FAIRy stories: the FAIR Data principles in theory and in practice The ‘FAIR Guiding Principles for scientific data management and stewardship’ [1] launched a global dialogue within research and policy communities and started a journey to wider accessibility and reusability of data and preparedness for automation-readiness (I am one of the army of authors). Over the past 5 years FAIR has become a movement, a mantra and a methodology for scientific research and increasingly in the commercial and public sector. FAIR is now part of NIH, European Commission and OECD policy. But just figuring out what the FAIR principles really mean and how we implement them has proved more challenging than one might have guessed. To quote the novelist Rick Riordan “Fairness does not mean everyone gets the same. Fairness means everyone gets what they need”. As a data infrastructure wrangler I lead and participate in projects implementing forms of FAIR in pan-national European biomedical Research Infrastructures. We apply web-based industry-lead approaches like Schema.org; work with big pharma on specialised FAIRification pipelines for legacy data; promote FAIR by Design methodologies and platforms into the researcher lab; and expand the principles of FAIR beyond data to computational workflows and digital objects. Many use Linked Data approaches. In this talk I’ll use some of these projects to shine some light on the FAIR movement. Spoiler alert: although there are technical issues, the greatest challenges are social. FAIR is a team sport. Knowledge Graphs play a role – not just as consumers of FAIR data but as active contributors. To paraphrase another novelist, “It is a truth universally acknowledged that a Knowledge Graph must be in want of FAIR data.” [1] Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18

2015 balti-and-bioinformatics

c.titus.brown

This document discusses ways to incentivize scientists to share their data through self-interest. It describes two existing models where data sharing is successful: oceanographic research consortia that require data sharing, and biomedical research projects that organize data generation and sharing through a common platform. The document proposes a distributed graph database and computing platform that would allow researchers to query diverse public and private datasets, providing immediate returns for data sharing. By making others' data useful to analyze and mine, researchers would be competitively disadvantaged not to share their own data. The goal is to enable open sharing by addressing current problems and remaining agile for future needs.

Executive Summary - Data Management Hub

Denis Parfenov

- DaMaHub is a distributed platform and local client that allows scientists to organize, share, and preserve their research data and results in an easy and secure way. - It employs blockchain and IPFS technologies to make scientific data findable, accessible, interoperable, and reusable while preserving authenticity. - As both a distributed platform and local client, DaMaHub integrates into researchers' workflows and makes data management and open sharing simple.

Data science and privacy regulation

blogzilla

This document summarizes key points about data science and privacy regulation: 1. Regulation aims to alter behavior according to standards to achieve defined outcomes, and can involve standard-setting, information gathering, and modifying behavior. 2. With "big data", problems arise for the laissez-faire conception of privacy regulation due to market failures, insider threats, and mass surveillance capabilities. 3. Designing for privacy is important, such as data minimization, decentralization, consent requirements, and easy-to-use privacy interfaces. The "data exhaust" from ubiquitous data collection threatens privacy in Europe.

ESA Ignite talk on UC3 Dash platform for data sharing

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Addressing the New Challenges in Data Sharing: Large-Scale Data and Sensitive...

Merce Crosas

This document discusses the challenges of sharing large-scale and sensitive data and approaches to address them. It describes how data sharing needs to continue supporting discovery, citation, access and reuse of data as datasets increase in size from GBs to TBs and PBs. Current collaborations are working on integrating large datasets with Dataverse and moving computing resources closer to data storage. The document also discusses the DataTags system for sharing sensitive data while maintaining privacy and security.

CDL Tools for DataCite 2014

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enabling communities of researchers working together across institutional bou...

Brian Bot

Sage Bionetworks is a non-profit organization that pilots various components to build a scientific research commons and enable more open and collaborative biomedical research. It supports ~40 employees working on research platform development and leadership. Sage brings together researchers across disciplines and institutions through initiatives like the Pan-Cancer Atlas Consortium and the CommonMind Consortium to facilitate data sharing and collaborative analysis around common questions.

(Bio)Hackathons

Benjamin Good

This document discusses biohackathons, which are events where participants collaborate to create new tools or applications in bioscience over a weekend. It provides examples of two biohackathons held in 2014 at Stanford University and University of California San Diego. At these events, around 30 participants formed teams and worked on projects like tools for visualizing and editing bio data or finding similarities between biomedical concepts. The goals were to foster collaborative creativity, learning, and networking. The best projects received small cash prizes.

A Big Picture in Research Data Management

Carole Goble

Policy Lunchbox - Digital Science

Kaitlin Thaney

This document summarizes digital science and the future of online research. It discusses how technology is changing the research workflow by enabling more efficient sharing of ideas, literature reviews, results, materials and data. However, there are still roadblocks to overcome, including specialization of tools, lack of interoperability, and accessibility issues. The key constituencies that must be considered are machines/tools, researchers, and decision makers. While the future of research is digital, adoption remains uneven and cultural shifts are needed to fully realize the benefits of new technologies.

What's hot

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Bionimbus: Towards One Million Genomes (XLDB 2012 Lecture)

Robert Grossman

A First Step Towards Content Protecting Plagiarism Detection

Scientific Information Analytics Group, Prof. Gipp

Enabling knowledge management in the Agronomic Domain

Pierre Larmande

Contractor-Borner-SNA-SAC

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Making the web work for science - eResearch nz

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Paul Groth

Sci Know Mine 2013: What can we learn from topic modeling on 350M academic do...

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Uc3 pasig-asis&t-2013-08-20-support-of-data-intensive-research

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2015 balti-and-bioinformatics

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Executive Summary - Data Management Hub

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Data science and privacy regulation

blogzilla

ESA Ignite talk on UC3 Dash platform for data sharing

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Addressing the New Challenges in Data Sharing: Large-Scale Data and Sensitive...

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CDL Tools for DataCite 2014

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enabling communities of researchers working together across institutional bou...

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(Bio)Hackathons

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What's hot (20)

Nicole Nogoy's talk at eResearchNZ 2014: Improving data sharing, integration ...

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Nicole Nogoy: GigaScience...how licensing can change the way we do research

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Sci Know Mine 2013: What can we learn from topic modeling on 350M academic do...

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FAIRy stories: the FAIR Data principles in theory and in practice

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ESA Ignite talk on UC3 Dash platform for data sharing

Addressing the New Challenges in Data Sharing: Large-Scale Data and Sensitive...

CDL Tools for DataCite 2014

enabling communities of researchers working together across institutional bou...

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Kaitlin Thaney discusses making science more open and reproducible through open web technologies. She defines open science as making the entire research process transparent by sharing hypotheses, protocols, data, and analysis. However, current research practices and systems do not fully support open science. Barriers include a lack of access to materials, difficulty publishing negative results, and the "digital skills gap" of many researchers. Promoting a culture of open source through training, community building, and interoperable standards and platforms can help shift practices towards more open and reproducible science at scale.

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Kaitlin Thaney

The document discusses increasing digital literacy for science researchers by teaching them best practices for open and reproducible science. It notes that self-education on digital tools does not scale effectively. The Mozilla Foundation is working with over 100 instructors to provide bootcamp trainings in digital and reproducible research skills to thousands of learners. It asks what core competencies are needed for open science and how to build broader capacity through assessment and participation in their efforts and online conversations.

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The need for disruption in science - Envisioning the Future, CIA

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Digital Science

1. kaitlin thaney mgr, external partnerships

2. xi. background

3. technology company publisher link london, nyc, tokyo

4. 1. science is changing

6. research idea publish lit review share results materials retest analyze experiment collect data

7. blocking points (to name a few ... ) idea publish lit review share results materials retest analyze experiment collect data

8. text text text

9. discovery & delivery content data materials

10. changes at the workbench knowledge exchange materials, machines ordering automation

11. annotation & curation machine-readable ontologies metadata

12. social & administrative P&T systems / funding reputation policy making incentives / impact factor

13. gaps still exist

14. remaining roadblocks specialisation of tools (+/-) interoperability accessibility design decisions the “social issue”

15. efficient search discoverability information management analysis incentives

16. 2. focus areas

17. (3)

18. knowledge discovery software applications research management

19. knowledge discovery software applications research management

20. steps towards better annotation and search

21.

22.

23.

24.

25.

26.

27.

28. text text text

29. can fine-tune

30. name disambiguation

31. 10,11-dihydro-5-methyl-5H-dibenzo[b,e][1,4]diazepin-11-one

32. knowledge discovery software applications research management

33. CC-BY-2.0 - Plaxco Lab - http://www.flickr.com/photos/34857812@N04/

34.

35. better tracking is needed

36.

37.

38.

39. knowledge discovery software applications research management

40. data capture (of a different sort)

41. imperfect system

42. “Right now we're going through a Cambrian explosion of metrics.” - Johan Bollen Nature 465, 864-866 (2010) | doi:10.1038/465864a

43. in terms of sheer volume and type

44. citation / impact factor h - index weighted citations (eigenfactor, sjr) “betweenness centrality” alt-metrics, etc.

45. difficult to ... harmonise track /maintain reward (even still measure)

46.

47.

48.

49. 3. the business

50. investment arm incubator role in-house dev

51. tiered approach build to scale researcher-focused

52. RAE teams pharma startups tools developers content providers partners/collaborators big business funding agencies researchers grant officers administrators

53. RAE teams pharma startups tools developers content providers work with us big business funding agencies researchers grant officers administrators

54. 4. our goal

55. software that understands science

56. software that understands scientists

57. more efficient research increase productivity better science

58. thank you. k.thaney@digital-science.com www.digital-science.com