Anita de Waard from Elsevier discussed research data management from a publisher's perspective. She outlined tools her organization has developed to enable open and integrated RDM, including metrics to measure data usage. While tools see adoption, challenges include a lack of researcher urgency, distributed responsibility for RDM, integrating many available tools, and unclear business models. She welcomed questions on her organization's role in supporting best practices.
Presentation by Ruth Wilson on Nature Publishing Group's Scientific Data journal given at the Now and Future of Data Publishing Symposium, 22 May 2013, Oxford, UK
[4.1] Data Citation and DOI's - Research Data Management - part of PhD course...3TU.Datacentrum
Training about Data Archive
You will learn:
What data citation is, and what the benefits are.
How to use DOIs for data citation.
How to cite a dataset
How to find publications with DOIs
Link your publications to your dataset (and vise versa) using DOIs
Presentation by Ruth Wilson on Nature Publishing Group's Scientific Data journal given at the Now and Future of Data Publishing Symposium, 22 May 2013, Oxford, UK
[4.1] Data Citation and DOI's - Research Data Management - part of PhD course...3TU.Datacentrum
Training about Data Archive
You will learn:
What data citation is, and what the benefits are.
How to use DOIs for data citation.
How to cite a dataset
How to find publications with DOIs
Link your publications to your dataset (and vise versa) using DOIs
The scientific and economic value of research data is enormous. To ensure successful subsequent usage, the scientific community needs efficient access to data, the data has to be reliable and persistent, and the quality of the data has to be proved.
One solution to these preconditions is to apply the techniques of today’s scientific publishing to research data. Besides its publication in a data repository together with some metadata, the data should undergo a transparent public peer-review using a publication platform.
The presentation discusses two approaches. On the one hand, the data can be the basis for a research article and undergoes a review parallel to the review of the manuscript. The data is then a reviewed supplement to a scientific publication. On the other hand, the data itself can be the subject of a publication whose quality is then assured by peers.
The presentation provides practical experience, especially with the latter strategy, realized through an established open access journal.
The challenge of sharing data well, how publishers can helpVarsha Khodiyar
Researchers, academic institutes and funders are increasingly recognizing the importance of data sharing for reproducible science. However, it is not always straightforward and clear to researchers as to how best to share data in a useful way. At Springer Nature we are working on several initiatives to help facilitate the sharing of research data in a reusable way, with our overarching goal being to publish research that is robust and reproducible. I will talk about the effort that goes into our flagship data journal, Scientific Data, to facilitate best practices in publication and sharing of research data, and share some of our experiences publishing Challenge datasets. I will also describe some of the newer Research Data Services that are now available to help all researchers (not only Springer Nature authors) to share their data in a useful way.
A template for a basic data management plan. Handout to accompany the presentations Introduction to Research Data Management and Preparing Your Research Data for the Future.
Presentation given at the British Library Turing workshop on Software Citation, considering what lessons could be learned from the world of data citation
Lesson 2 in a set of 10 created by DataONE on Best Practices fo Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
This presentation was provided by Patricia Payton of Proquest during the NISO webinar, Engineering Access Under the Hood, Part Two, held on November 15, 2017.
Annotation examples. This is an overview of some of the software I have used for annotation (and a few extra features some of this software has.) This was presented in the SwissUniversities Doctoral Programme, Language & Cognition, in the Module: Linguistic and corpus perspectives on argumentative discourse.
Screenshots are given of GATE, UAM Corpus Tool, Excel, BRAT, EPPI Reviewer, and a custom tool. In most cases there are references to one of my papers for further details.
I briefly describe a typical annotation process:
Find text of interest
Find phenomena of interest
Draft an annotation manual
Iteratively test annotation & revise manual
Find questionable annotations, check disagreements.
Revise the manual.
Iterate.
Annotate
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Using data management plans as a research tool: an introduction to the DART Project
Amanda L. Whitmire, Ph.D., Assistant Professor, Data Management Specialist, Oregon State University Libraries & Press
Lawrence-f1000-publishing with data-nfdp13DataDryad
Presentation by Rebecca Lawrence on F1000's initiatives for publishing with data given at the Now and Future of Data Publishing Symposium, 22 May 2013, Oxford, UK
Transparency and reproducibility in researchLouise Corti
Talk given at the ESS Summer School: An introduction to using big data in the social sciences, 20-24 July 2020, University of Essex, Colchester, UK.
In the morning we look at publishing and sharing data and the importance of research replication, code sharing, examining what methodological issues peer reviewers might look for in a published paper using big data. An increasing number of journals in the sciences and social sciences expect a high degree of transparency and knowing how best to publish high quality raw (or processed data), methodology and code is a useful skill. We show how ‘data papers’ help to elucidate how datasets were constructed, compiled and processed, and help to showcase the value of data beyond the original research.
In early 2014, we asked science and social science researchers...
• What expectations do the terms publication and peer review raise in reference to data?
• What features would be useful to evaluate the trustworthiness, evaluate the impact, and enhance the prestige of a data publication?
IDCC Workshop: Analysing DMPs to inform research data services: lessons from ...Amanda Whitmire
A workshop as part of the International Digital Curation Conference 2016 on DMP development and support. This presentation demonstrates how we can use data management plans as a source of information to better understand researcher data stewardship practices and how to support them. Be sure to see the slide notes to better understand the presentation (most slides are just photos/icons).
Elaine Martin, MSLS, DA, Donna Kafel, RN, MSLS, and Andrew Creamer, MaEd, MSLS of UMass Medical School''s Lamar Soutter Library present Best Practices for Managing Data. The presentation features the importance of managing data for research projects, and tactical best practice initiatives to create a data management and sharing plan, including how to preserve label, secure, store, and preserve data. Issues, such as licensing, data dictionaries, regulations, and metadata are addressed in the presentation.
Presentation to IASSIST 2013, in the session Expanding Scholarship: Research Journals and Data Linkages. Describes PREPARDE workshop on repository accreditation for data publication and invites comments on guidelines.
The scientific and economic value of research data is enormous. To ensure successful subsequent usage, the scientific community needs efficient access to data, the data has to be reliable and persistent, and the quality of the data has to be proved.
One solution to these preconditions is to apply the techniques of today’s scientific publishing to research data. Besides its publication in a data repository together with some metadata, the data should undergo a transparent public peer-review using a publication platform.
The presentation discusses two approaches. On the one hand, the data can be the basis for a research article and undergoes a review parallel to the review of the manuscript. The data is then a reviewed supplement to a scientific publication. On the other hand, the data itself can be the subject of a publication whose quality is then assured by peers.
The presentation provides practical experience, especially with the latter strategy, realized through an established open access journal.
The challenge of sharing data well, how publishers can helpVarsha Khodiyar
Researchers, academic institutes and funders are increasingly recognizing the importance of data sharing for reproducible science. However, it is not always straightforward and clear to researchers as to how best to share data in a useful way. At Springer Nature we are working on several initiatives to help facilitate the sharing of research data in a reusable way, with our overarching goal being to publish research that is robust and reproducible. I will talk about the effort that goes into our flagship data journal, Scientific Data, to facilitate best practices in publication and sharing of research data, and share some of our experiences publishing Challenge datasets. I will also describe some of the newer Research Data Services that are now available to help all researchers (not only Springer Nature authors) to share their data in a useful way.
A template for a basic data management plan. Handout to accompany the presentations Introduction to Research Data Management and Preparing Your Research Data for the Future.
Presentation given at the British Library Turing workshop on Software Citation, considering what lessons could be learned from the world of data citation
Lesson 2 in a set of 10 created by DataONE on Best Practices fo Data Management. The full module can be downloaded from the DataONE.org website at: http://www.dataone.org/educaiton-modules. Released under a CC0 license, attribution and citation requested.
This presentation was provided by Patricia Payton of Proquest during the NISO webinar, Engineering Access Under the Hood, Part Two, held on November 15, 2017.
Annotation examples. This is an overview of some of the software I have used for annotation (and a few extra features some of this software has.) This was presented in the SwissUniversities Doctoral Programme, Language & Cognition, in the Module: Linguistic and corpus perspectives on argumentative discourse.
Screenshots are given of GATE, UAM Corpus Tool, Excel, BRAT, EPPI Reviewer, and a custom tool. In most cases there are references to one of my papers for further details.
I briefly describe a typical annotation process:
Find text of interest
Find phenomena of interest
Draft an annotation manual
Iteratively test annotation & revise manual
Find questionable annotations, check disagreements.
Revise the manual.
Iterate.
Annotate
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Using data management plans as a research tool: an introduction to the DART Project
Amanda L. Whitmire, Ph.D., Assistant Professor, Data Management Specialist, Oregon State University Libraries & Press
Lawrence-f1000-publishing with data-nfdp13DataDryad
Presentation by Rebecca Lawrence on F1000's initiatives for publishing with data given at the Now and Future of Data Publishing Symposium, 22 May 2013, Oxford, UK
Transparency and reproducibility in researchLouise Corti
Talk given at the ESS Summer School: An introduction to using big data in the social sciences, 20-24 July 2020, University of Essex, Colchester, UK.
In the morning we look at publishing and sharing data and the importance of research replication, code sharing, examining what methodological issues peer reviewers might look for in a published paper using big data. An increasing number of journals in the sciences and social sciences expect a high degree of transparency and knowing how best to publish high quality raw (or processed data), methodology and code is a useful skill. We show how ‘data papers’ help to elucidate how datasets were constructed, compiled and processed, and help to showcase the value of data beyond the original research.
In early 2014, we asked science and social science researchers...
• What expectations do the terms publication and peer review raise in reference to data?
• What features would be useful to evaluate the trustworthiness, evaluate the impact, and enhance the prestige of a data publication?
IDCC Workshop: Analysing DMPs to inform research data services: lessons from ...Amanda Whitmire
A workshop as part of the International Digital Curation Conference 2016 on DMP development and support. This presentation demonstrates how we can use data management plans as a source of information to better understand researcher data stewardship practices and how to support them. Be sure to see the slide notes to better understand the presentation (most slides are just photos/icons).
Elaine Martin, MSLS, DA, Donna Kafel, RN, MSLS, and Andrew Creamer, MaEd, MSLS of UMass Medical School''s Lamar Soutter Library present Best Practices for Managing Data. The presentation features the importance of managing data for research projects, and tactical best practice initiatives to create a data management and sharing plan, including how to preserve label, secure, store, and preserve data. Issues, such as licensing, data dictionaries, regulations, and metadata are addressed in the presentation.
Presentation to IASSIST 2013, in the session Expanding Scholarship: Research Journals and Data Linkages. Describes PREPARDE workshop on repository accreditation for data publication and invites comments on guidelines.
Preparing your data for sharing and publishingVarsha Khodiyar
Talk given as part of the MRC Cognition and Brain Sciences Unit Open Science Day on 20th November 2018 , University of Cambridge (https://www.eventbrite.co.uk/e/open-science-day-at-the-mrc-cbu-tickets-50363553745)
Presentation slides on Open Science and research reproducibility. Presented by Gareth Knight (LSHTM Research Data Manager) on 18th September 2018, as part of an Open Science event for LSHTM Week 2018.
Overview of the Research on Open Educational Resources for Development (ROER4D) Open Data initiative, highlighting data management principles, the five pillars of the ROER4D data publication approach and the project de-identification approach.
The ROER4D Curation & Dissemination team provides an overview of the ROER4D open data initiative as well as some key insights and challenges experienced.
Presentation on data sharing that outlines five layers that must be addressed to enable data to be located, obtained, access, understood and use, and cited.
This presentation introduced participants to the DC 101 course and was given at the Digital Curation and Preservation Outreach and Capacity Building Workshop in Belfast on September 14-15 2009.
http://www.dcc.ac.uk/events/workshops/digital-curation-and-preservation-outreach-and-capacity-building-workshop
Aim:- To show how research data management can contribute to the success of your PhD.
*What is research data and why it is important?
*The Research Data lifecycle
* Research Data – more than just your results
* FAIR data and Open Research
* DMP online tool
Talk at the World Science Festival at Columbia, June 2, 2017: session on Big Data and Physics: http://www.worldsciencefestival.com/programs/big-data-future-physics/
Data Repositories: Recommendation, Certification and Models for Cost RecoveryAnita de Waard
Talk at NITRD Workshop "Measuring the Impact of Digital Repositories" February 28 – March 1, 2017 https://www.nitrd.gov/nitrdgroups/index.php?title=DigitalRepositories
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
1. Research Data Management
From A Publisher’s
Perspective
Presentation for RDMI Meeting, Industry Panel
September 14, 2017
Anita de Waard, a.dewaard@elsevier.com
VP Research Data Management, RDS Elsevier
2. Outline:
1. How has your work in data management enabled research and
discovery?
2. What key areas of success has your organization achieved in
delivering research data management solutions?
3. What are the greatest challenges you are facing in developing
solutions that meet the needs of research data management?
3. 10.Integrateupstreamanddownstream
–makemetadatatoserveuse.
Save
Share
Use
9. Re-usable (allow tools to run on it)
8. Reproducible
7. Trusted (e.g. reviewed)
6. Comprehensible (description / method is available)
5. Citable
4. Discoverable (data is indexed or data is linked from article)
3. Accessible
1. Stored (existing in some form)
2. Preserved (long-term & format-independent)
10 Properties of Highly Effective Research Data
4. 10.Integrateupstreamanddownstream
–makemetadatatoserveuse.
Save
Share
Use
9. Re-usable (allow tools to run on it)
8. Reproducible
7. Trusted (e.g. reviewed)
6. Comprehensible (description / method is available)
5. Citable
4. Discoverable (data is indexed or data is linked from article)
3. Accessible
1. Stored (existing in some form)
2. Preserved (long-term & format-independent)
Hivebench
Lab Notebook
Mendeley
Data Repository
DataSearch
Data Journals:
Research Elements
Research Data
Guidelines for
Journal
10 Properties of Highly Effective Research Data
Repeat.
Replicate.
Reproduce.
Reuse.
Review.
5. HIvebench: Store protocols in an Electronic Lab Notebook.
Keep collection
of protocols
online
Edit, export,
share
https://www.hivebench.com/
6. Hivebench: Run experiments from this Lab Notebook.
Edit, export,
share
Base on saved
Protocols
Save and
Export Outputs
https://www.hivebench.com/
7. https://data.mendeley.com/
Mendeley Data: Export results to a trusted data repository.
Describe how
exoeriment can
be reproduced
Keep track of
versions of
dataset
Create DOI for
Citation
Link back to
protocols
Store up to 5
GB of data in
many formats
9. Data With Journals: Research Data Guidelines For Journals:
https://www.elsevier.com/authors/author-services/research-data/data-guidelines
Option A: Research Data deposit and citation
You are encouraged to:
• Deposit your research data in a relevant data repository
• Cite this dataset in your article
Option B: Research Data deposit, citation and linking
(or Research Data Availability Statement)
You are encouraged to:
• Deposit your research data in a relevant data repository
• Cite and link to this dataset in your article
• If this is not possible, make a statement explaining why research data cannot be shared
Option C: Research Data deposit, citation and linking
(or Research Data Availability Statement)
You are required to:
• Deposit your research data in a relevant data repository
• Cite and link to this dataset in your article
• If this is not possible, make a statement explaining why research data cannot be shared
Option D: Research Data deposit, citation and linking
You are required to:
• Deposit your research data in a relevant data repository
• Cite and link to this dataset in your article
Option E: Research Data deposit, citation and linking
(or Research Data Availability Statement);
You are required to:
• Deposit your research data in a relevant data repository
• Cite and link to this dataset in your article.
• If this is not possible, make a statement explaining why research data cannot be shared
• Peer reviewers are asked to review the data prior to publication
11. Reproducibility: Reproducibility Papers
we have implemented a new publication model for the Reproducibility Section of Information Systems Journal. In this
section,
authors submit a reproducibility paper that explains in detail the computational assets from a previous published
manuscript in Information Systems. Submission is by invitation only.
To increase the practice of reproducibility in computational science, we have two main goals:
1. Usability: development of tools that make it easier and significantly less time-consuming for authors to do
reproducible research, and for reviewers to execute computational artifacts (and modify them) corresponding to
published results.
2. Incentives: a new publication model that recognizes the efforts of making experiments reproducible (for authors)
and verifying published scientific results (for reviewers).
Using Mendeley Data authors also submit their code, data, and optionally a ReproZip package or a Docker
container to make the review process easier. Reviewers not only review the reproducibility paper, but also validate
the results and claims published in the original manuscript.
Once the paper is accepted, reviewers also become co-authors and are encouraged to add a section in the paper that
states the extent to which the software is portable, is robust to changes, and is likely to be usable as a subcomponent
or as a basis for comparison by future researchers. The review is not blinded, so authors and reviewers are
encouraged to engage in a discussion about the validity of the experimental results as many times as
necessary.
12. Currently In Development: Mendeley Data Management Platform:
Integration with Existing Standards/Systems at Institution
13. Underway: “Basket of Metrics” & Elsevier Tracking Solutions
Goal: Metric: How to measure
More data is saved:
1 Stored, i.e. safely available in long-term
repository)
Nr of datasets stored in long-term storage MD, Pure; Plum Indexes
Figshare, Dryad, MD and
working on Dataverse.
2. Published, i.e. long-term preserved,
accessible via web, have a GUID, citeable, with
proper metadata
Nr of datasets published, in some form Scholix,
ScienceDirect/Scopus
3. Linked, to articles or other datasets Nr of datasets linked to articles Scholix, Scopus
4. Validated, by a reviewer/curated Nr of datasets in curated databases/peer
reviewed in data articles
Science Direct, DataSearch
(for curated Dbses)
More data is seen and used:
5. Discovered: found by users Nr of datasets viewed in
databases/websites/search engines
Datasearch, metrics from
other search
engines/repositories
6. Identified: Resolved through a GUID Broker DOI is resolved DataCite has DOI resolution:
made available?
7. Mentioned: Social media and news Social media and news mentions Plum and Newsflo
8. Cited: Formal citations of data Nr of datasets cited in articles Scopus
9. Downloaded: Distinct downloads Downloaded from repositories Downloads from MD, access
data from Figshare/Dryad
10. Reused: Dataset is used for new research Mention of usage in article or other
dataset
SD, access to other data
repositories
14. 886 random articles checked
570 articles without any supplementary/associated data
(64%); +151 articles with supplementary docs (but not data)
2 data journal articles (0.2%)
86 articles with associated data in repositories (9.7%)
81 articles linked to associated data in a repository (9.1%)
5 articles with no link to a repository (0.6%)
79 articles with supplementary data (8.9%)
9. Re-usable
8. Reproducible
7. Trusted
6. Comprehensible
5. Citable
4. Discoverable
3. Accessible
2. Preserved
1. Stored
8.9%
9.1%
0.6%
0.2%
0
10
20
30
40
50
60
70
80
90
Number of articles with linked data deposited in a data
repository for 2015-2017/n=81
Total
Random Selection
Articles 886
Links found manually 81
Links found through
Scholix 5
Total links 86 (9.8%)
We need baselines! Example: University of Manchester
Data sharing = 19% (well above the average of 5.5%)
Courtesy Sean Husen and Helena Cousijn (Elsevier)
15. Open Data Report Reveals Some Challenges:
https://data.mendeley.com/datasets/bwrnfb4bvh/1
Data sharing survey (with 1167 respondents):
• Although 69% of respondents found that sharing data was
very important in their field
• And 73% wanted to have access to other people’s data,
• Only 37% believe there was credit in doing so,
• And only 25% felt they had adequate training to properly
share their data with others.
The main barriers for sharing data were:
• privacy concerns,
• ethical issues,
• intellectual property rights issues.
Furthermore:
• Mandates from publishers or funding agencies were largely
not seen as a driving force
=> Gap between desire and practice concerning data sharing.
17. Further Challenge: How do you ‘Play Well With Others’ when there
are so many others (e.g. 47 tools on NDS Labs Workbench) and
they are mostly ‘academic’ (i.e. OS, constantly renewed, etc etc)?
18. Summary:
1. How has your work in data management enabled research and discovery?
• Providing a suite of tools and standards that encourage open, integrated RDM
solutions.
2. What key areas of success has your organization achieved in delivering
research data management solutions?
• Tools are used (ergo: useful);
• Developing institutional solutions and data metrics with partners.
3. What are the greatest challenges you are facing in developing solutions
that meet the needs of research data management?
• No great urgency for researchers, inadequate knowledge of possibilities;
• Distributed responsibility/decision-making processes for RDM;
• Plethora of tools to integrate with;
• Difficult to see what the market is (OS, completely? Academic/government?)
• > How can publisher play a role?
Feel free to email me with any questions!
a.dewaard@elsevier.com