This presentation explains how us, at UNEP-MAP, deal with data: contextualizing the adopted data policy (http://193.206.192.248/en/communication/event/second-info-rac-national-focal-point-meeting/wg512_2_eng-3.pdf), identifying crucial points and possible barriers to data opening in the Mediterranean basin.
Stuart Macdonald steps through the process of creating a robust data management plan for researchers. Presented at the European Association for Health Information and Libraries (EAHIL) 2015 workshop, Edinburgh, 11 June 2015.
Presentation during the 14th Association of African Universities (AAU) Conference and African Open Science Platform (AOSP)/Research Data Alliance (RDA) Workshop in Accra, Ghana, 7-8 June 2017.
Stuart Macdonald steps through the process of creating a robust data management plan for researchers. Presented at the European Association for Health Information and Libraries (EAHIL) 2015 workshop, Edinburgh, 11 June 2015.
Presentation during the 14th Association of African Universities (AAU) Conference and African Open Science Platform (AOSP)/Research Data Alliance (RDA) Workshop in Accra, Ghana, 7-8 June 2017.
Data accessibility and the role of informatics in predicting the biosphereAlex Hardisty
The variety, distinctiveness and complexity of life – biodiversity in other words and by implication the ecosystems in which it is situated – is our life support system. It is absolutely essential and more important than almost everything else but it is typically taken for granted. Today’s big societal challenges – food and water security, coping with environmental change and aspects of human health – are beyond the abilities of any one individual or research group to solve. Solving them depends not only on collaboration to deliver the appropriate scientific evidence but increasingly on vast amounts of data from multiple sources (environmental, taxonomic, genomic and ecological) gathered by manual observation and automated sensors, digitisation, remote sensing, and genetic sequencing. In April 2012 we called the biodiversity and ecosystems research communities to arms to formulate a consensus view on establishing an infrastructure to improve the accessibility of the ever-increasing volumes of biological data. We published the whitepaper: “A decadal view of biodiversity informatics: challenges and priorities” that has since been viewed more than 24,000 times. We envisage a shared and maintained multi-purpose network of computationally-based processing services sitting on top of an open data domain. By open data domain we mean data that is accessible i.e., published, registered and linked. BioVeL, pro-iBiosphere, ViBRANT and other FP7 funded projects have all explored aspects of this vision.
Digital transformation to enable a FAIR approach for health data scienceVarsha Khodiyar
Invited talk for ConTech Pharma on 1st March 2022
Abstract
Health Data Research UK is the UK’s national institute for health data science, with a mission to unite the UK’s health data to enable discoveries that improve people’s lives. In this talk, Dr Varsha Khodiyar will outline how HDR UK is bringing together disparate health data from all four countries of the United Kingdom, creating the infrastructure to enable discovery of and access to health data, and the convening standards making bodies to improve data linkage and data reuse. Varsha will also discuss how HDR UK is moving beyond the traditional confines of FAIR data to also ensure that data sharing and data use is transparent and ‘fair’ for the patients and lay public who are the subjects of these datasets.
Data management plans existed long before the NSF started requiring them. DMPs have inherent value despite their being relatively unknown to researchers until now. Proper, thorough data management plans are potentially a major time saver and a huge asset for the project. In this webinar, we will cover how to go beyond funder requirements and develop more thorough data DMPs The Gulf of Mexico Research Initiative requires an extensive data management plan for projects it funds; we will hear about their efforts and how they are planning to use the DMPTool going forward.
Data Sharing Principles and Legal Interoperability for Essential Biodiversity...agosti
Lightning talk by Egloff and Agosti, Plazi at the GLOBIS-B workshop, Leipzig, February 2016.
The proposed policy reflects the authors view and is not the agreed policy within Globis-B.
Presentation on INSPIRE and Higher Education (1 of 2)JISC GECO
Presentation designed to explain the relationship between academic data and the EU INSPIRE Directive. Produced by staff from EDINA and the Digital Curation Centre.
FAIR data: what it means, how we achieve it, and the role of RDASarah Jones
Presentation on FAIR data, the FAIR Data Action Plan developed by the European Commission Expert Group and the role of the Research Data Alliance on implementing FAIR. The presentation was given at the RDAFinland workshop held on 6th June - https://www.csc.fi/web/training/-/rda_and_fair_supporting_finnish_researchers
Presented at the:
Canadian Aviation Safety Collaboration Forum
International Civil Aviation Organization (ICAO)
Montreal, QC
January 23, 2019
This presentation was made in real-time while attending the Forum. The objective was to observe and listen, and share some examples outside of this community that may provide insight about data sharing models with a focus on governance.
Big Data Europe: SC6 Workshop 3: The European Research Data Landscape: Opport...BigData_Europe
Slides of the keynote at the 3rd Big Data Europe SC6 Workshop co-located at SEMANTiCS2018 in Amsterdam (NL) on: The European Research Data Landscape: Opportunities for CESSDA by Peter Doorn, Director DANS, Chair, Science Europe W.G. on Research Data. Chair, CESSDA ERIC General Assembly
Securing, storing and enabling safe access to dataRobin Rice
Invited talk as part of Westminster Insight Research Data Management Forum, https://www.westminsterinsight.co.uk/event/3416/Research_Data_Management_Forum
Data accessibility and the role of informatics in predicting the biosphereAlex Hardisty
The variety, distinctiveness and complexity of life – biodiversity in other words and by implication the ecosystems in which it is situated – is our life support system. It is absolutely essential and more important than almost everything else but it is typically taken for granted. Today’s big societal challenges – food and water security, coping with environmental change and aspects of human health – are beyond the abilities of any one individual or research group to solve. Solving them depends not only on collaboration to deliver the appropriate scientific evidence but increasingly on vast amounts of data from multiple sources (environmental, taxonomic, genomic and ecological) gathered by manual observation and automated sensors, digitisation, remote sensing, and genetic sequencing. In April 2012 we called the biodiversity and ecosystems research communities to arms to formulate a consensus view on establishing an infrastructure to improve the accessibility of the ever-increasing volumes of biological data. We published the whitepaper: “A decadal view of biodiversity informatics: challenges and priorities” that has since been viewed more than 24,000 times. We envisage a shared and maintained multi-purpose network of computationally-based processing services sitting on top of an open data domain. By open data domain we mean data that is accessible i.e., published, registered and linked. BioVeL, pro-iBiosphere, ViBRANT and other FP7 funded projects have all explored aspects of this vision.
Digital transformation to enable a FAIR approach for health data scienceVarsha Khodiyar
Invited talk for ConTech Pharma on 1st March 2022
Abstract
Health Data Research UK is the UK’s national institute for health data science, with a mission to unite the UK’s health data to enable discoveries that improve people’s lives. In this talk, Dr Varsha Khodiyar will outline how HDR UK is bringing together disparate health data from all four countries of the United Kingdom, creating the infrastructure to enable discovery of and access to health data, and the convening standards making bodies to improve data linkage and data reuse. Varsha will also discuss how HDR UK is moving beyond the traditional confines of FAIR data to also ensure that data sharing and data use is transparent and ‘fair’ for the patients and lay public who are the subjects of these datasets.
Data management plans existed long before the NSF started requiring them. DMPs have inherent value despite their being relatively unknown to researchers until now. Proper, thorough data management plans are potentially a major time saver and a huge asset for the project. In this webinar, we will cover how to go beyond funder requirements and develop more thorough data DMPs The Gulf of Mexico Research Initiative requires an extensive data management plan for projects it funds; we will hear about their efforts and how they are planning to use the DMPTool going forward.
Data Sharing Principles and Legal Interoperability for Essential Biodiversity...agosti
Lightning talk by Egloff and Agosti, Plazi at the GLOBIS-B workshop, Leipzig, February 2016.
The proposed policy reflects the authors view and is not the agreed policy within Globis-B.
Presentation on INSPIRE and Higher Education (1 of 2)JISC GECO
Presentation designed to explain the relationship between academic data and the EU INSPIRE Directive. Produced by staff from EDINA and the Digital Curation Centre.
FAIR data: what it means, how we achieve it, and the role of RDASarah Jones
Presentation on FAIR data, the FAIR Data Action Plan developed by the European Commission Expert Group and the role of the Research Data Alliance on implementing FAIR. The presentation was given at the RDAFinland workshop held on 6th June - https://www.csc.fi/web/training/-/rda_and_fair_supporting_finnish_researchers
Presented at the:
Canadian Aviation Safety Collaboration Forum
International Civil Aviation Organization (ICAO)
Montreal, QC
January 23, 2019
This presentation was made in real-time while attending the Forum. The objective was to observe and listen, and share some examples outside of this community that may provide insight about data sharing models with a focus on governance.
Big Data Europe: SC6 Workshop 3: The European Research Data Landscape: Opport...BigData_Europe
Slides of the keynote at the 3rd Big Data Europe SC6 Workshop co-located at SEMANTiCS2018 in Amsterdam (NL) on: The European Research Data Landscape: Opportunities for CESSDA by Peter Doorn, Director DANS, Chair, Science Europe W.G. on Research Data. Chair, CESSDA ERIC General Assembly
Securing, storing and enabling safe access to dataRobin Rice
Invited talk as part of Westminster Insight Research Data Management Forum, https://www.westminsterinsight.co.uk/event/3416/Research_Data_Management_Forum
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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.
Influence of Marketing Strategy and Market Competition on Business Plan
UNEP-MAP Data Policy in brief
1. The MAP Data Policy: implications for
Contracting Parties
October 2022
Annalisa Minelli – Knowledge Management Officer
annalisa.minelli@info-rac.org
2. Presentation Outline
• The approved MAP Data Policy: document, actors, objectives
• Principles behind prescriptions
• Implications for Contracting Parties
3. Data Policy: the document
Adoption of the Data Policy:
UNEP/MED IG.25/27, Decision IG.25/10 (p. 353)
• Approved during the Convention for the Protection of the
Marine Environment and the Coastal Region of the
Mediterranean (Barcelona Convention) and its Protocols
at their 22nd Meeting
• Aim to achieve a base level of legal interoperability
• Establishing base principles, objectives and means to
achieve this interoperability for each data flow belonging
to the MAP
4. Data Policy: actors
The decision (UNEP/MED IG.25/27, Decision IG.25/10) is about:
1. Adopt the United Nations Environment Programme/Mediterranean Action Plan (UNEP/MAP) Data Policy as set out in
Annex I to the present Decision;
2. Request the Secretariat (INFO/RAC) to provide the necessary technical support to Contracting Parties and to address
any needs identified to fully implement the UNEP/MAP Data Policy;
3. Call upon the Contracting Parties to take effective measures to implement the UNEP/MAP Data Policy.
5. Data Policy: actors
The decision (UNEP/MED IG.25/27, Decision IG.25/10) is about:
1. Adopt the United Nations Environment Programme/Mediterranean Action Plan (UNEP/MAP) Data Policy as set out in
Annex I to the present Decision;
2. Request the Secretariat (INFO/RAC) to provide the necessary technical support to Contracting Parties and to address
any needs identified to fully implement the UNEP/MAP Data Policy;
3. Call upon the Contracting Parties to take effective measures to implement the UNEP/MAP Data Policy.
What we are doing, as INFO/RAC
6. Data Policy: actors
The decision (UNEP/MED IG.25/27, Decision IG.25/10) is about:
1. Adopt the United Nations Environment Programme/Mediterranean Action Plan (UNEP/MAP) Data Policy as set out in
Annex I to the present Decision;
2. Request the Secretariat (INFO/RAC) to provide the necessary technical support to Contracting Parties and to address
any needs identified to fully implement the UNEP/MAP Data Policy;
3. Call upon the Contracting Parties to take effective measures to implement the UNEP/MAP Data Policy.
What the CP are supposed to
do, with the help of INFO/RAC
7. Data Policy: objectives
• availability of latest data and maintenance of long-term
series
• exploitation, re-use and re-combination of data from
different sources in different frameworks and media
• full, free and open access to all kinds of data, where
possible, whilst recognizing and respecting the variety of
business models and data ownerships
• protection of integrity, transparency, and traceability in
environmental data, analysis and forecasts
• recognition of data providers and of their intellectual
property rights through citation and data licenses
• meeting relevant national legislations and government
guidance on the management and distribution of
environmental information
• implementation of INSPIRE, SEIS principles, Copernicus
and GEOSS data sharing principles
• interoperability and use of standards
• use of crowd sourced and citizen science data
• recognition of the quality of data through quality
assurance and quality control procedures
• publication of relevant metadata
• stewardship and sharing of data from research projects.
Support – Promote – Enable
(p. 358)
8. Data Policy: objectives
Support
Promote
Enable
availability of latest data
and maintenance of
long-term series
re-use of data
from different
sources
full, free and
open access
protection of integrity,
transparency, and
traceability of
environmental data
recognition of
intellectual
property rights
meeting relevant
national legislations
implementation of INSPIRE, SEIS
principles, Copernicus and GEOSS
data sharing principles
interoperability and
use of standards
citizen
science data
quality assurance
and quality control
procedures
publication of
metadata
research data
stewardship
9. Data Policy: objectives
Support
Promote
Enable
availability of latest data
and maintenance of
long-term series
re-use of data
from different
sources
full, free and
open access
protection of integrity,
transparency, and
traceability of
environmental data
recognition of
intellectual
property rights
meeting relevant
national legislations
implementation of INSPIRE, SEIS
principles, Copernicus and GEOSS
data sharing principles
interoperability and
use of standards
citizen
science data
quality assurance
and quality control
procedures
publication of
metadata
research data
stewardship
Elements
involved
10. Data Policy: objectives
Support
Promote
Enable
availability of latest data
and maintenance of
long-term series
re-use of data
from different
sources
full, free and
open access
protection of integrity,
transparency, and
traceability of
environmental data
recognition of
intellectual
property rights
meeting relevant
national legislations
implementation of INSPIRE, SEIS
principles, Copernicus and GEOSS
data sharing principles
interoperability and
use of standards
citizen
science data
quality assurance
and quality control
procedures
publication of
metadata
research data
stewardship
Qualities of
the elements
11. Data Policy: objectives
Support
Promote
Enable
availability of latest data
and maintenance of
long-term series
re-use of data
from different
sources
full, free and
open access
protection of integrity,
transparency, and
traceability of
environmental data
recognition of
intellectual
property rights
meeting relevant
national legislations
implementation of INSPIRE, SEIS
principles, Copernicus and GEOSS
data sharing principles
interoperability and
use of standards
citizen
science data
quality assurance
and quality control
procedures
publication of
metadata
research data
stewardship
Constraints
12. Data Policy: objectives
Support
Promote
Enable
availability of latest data
and maintenance of
long-term series
re-use of data
from different
sources
full, free and
open access
protection of integrity,
transparency, and
traceability of
environmental data
recognition of
intellectual
property rights
meeting relevant
national legislations
implementation of INSPIRE, SEIS
principles, Copernicus and GEOSS
data sharing principles
interoperability and
use of standards
citizen
science data
quality assurance
and quality control
procedures
publication of
metadata
research data
stewardship
Principles
13. Principles behind prescriptions
GENERAL: Data should be managed as close as possible to its source, collected once, shared with others,
readily available to fulfil UNEP-MAP mandate. (p.356)
NO data duplication
YES fair and FAIR organization
Findable
Accessible
Interoperable
Reusable
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
14. Principles behind prescriptions
GENERAL: Data should be managed as close as possible to its source, collected once, shared with others,
readily available to fulfil UNEP-MAP mandate
NO duplication of efforts
YES exploitation of others’ work (where available)
15. Principles behind prescriptions
GENERAL: Data should be managed as close as possible to its source, collected once, shared with others,
readily available to fulfil UNEP-MAP mandate
NO data ownership
YES data stewardship
«Data is MINE and I preserve it on my PC»
«Data is a PUBLIC GOOD, I understand its
usefulness for everyone, I will do anything
I can to promote progress»
European Commission, Directorate-General for Research and Innovation, Open innovation, open science,
open to the world : a vision for Europe, Publications Office,
2016, https://data.europa.eu/doi/10.2777/061652
to
from
16. Principles behind prescriptions
A. Interoperability and use of Standards
Interoperability means that any piece of information can be
shared among multiple actors with the same quality level (quality
of instruments, quality of information, quality of elaboration)
This is possible only by means of Standards: «universally»
recognised rules to share information (protocols)
17. Principles behind prescriptions
B. Open Access
Data must be as open as possible, respecting the constraints
imposed by local legislation, sensitivity of data, and copyrights
Open Access means ensure the possibility
for less rich Countries to access knowledge
We don’t want a Two-speed
world, we want a Unique world
Fallout of data collected by means of public
funds should be available for the wide public
18. Principles behind prescriptions
B. Open Access
Data must be as open as possible, respecting the constraints
imposed by local legislation, sensitivity of data, and copyrights
To formalize Open Access we need
Open Data licenses:
• Public Domain (CC-0)
• Creative Commons Attribution (CC-
BY)
19. Principles behind prescriptions
B. Open Access
Data must be as open as possible, respecting the constraints
imposed by local legislation, sensitivity of data, and copyrights
To formalize Open Access we need
Open Data licenses:
• Public Domain (CC-0)
• Creative Commons Attribution (CC-
BY)
Data Policy
20. Principles behind prescriptions
B. Open Access
Data must be as open as possible, respecting the constraints
imposed by local legislation, sensitivity of data, and copyrights
To formalize Open Access we need
Open Data licenses:
• Public Domain (CC-0)
• Creative Commons Attribution (CC-
BY)
Eventually
sensitive
data
21. Principles behind prescriptions
C. Re-use of data
Since we don’t want to duplicate efforts, data must be
enabled to be reused, exploited and recombined from
different sources to different frameworks and media
To be Reusable:
• meta(data) are richly described with a plurality of
accurate and relevant attributes
• (meta)data are released with a clear and accessible data
usage license
• (meta)data are associated with detailed provenance
• (meta)data meet domain-relevant community standards
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
relevant attributes
license
provenance
standards
22. Principles behind prescriptions
C. Re-use of data
Since we don’t want to duplicate efforts, data must be
enabled to be reused, exploited and recombined from
different sources to different frameworks and media
To be Reusable:
• meta(data) are richly described with a plurality of
accurate and relevant attributes
• (meta)data are released with a clear and accessible data
usage license
• (meta)data are associated with detailed provenance
• (meta)data meet domain-relevant community standards
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
provenance
The provenance of data must be clear and stated
in the metadata: owner, contact person and
responsible for data must be clearly identified
23. Principles behind prescriptions
C. Re-use of data
Since we don’t want to duplicate efforts, data must be
enabled to be reused, exploited and recombined from
different sources to different frameworks and media
To be Reusable:
• meta(data) are richly described with a plurality of
accurate and relevant attributes
• (meta)data are released with a clear and accessible data
usage license
• (meta)data are associated with detailed provenance
• (meta)data meet domain-relevant community standards
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
relevant attributes
The choice of attributes is codified and often
stated in vocaboulary or standards. Relevant
attributes increase the value of data.
24. Principles behind prescriptions
D. INSPIRE, SEIS, Copernicus and GEOSS principles
• Data should be collected only once and kept where it can be maintained most effectively.
• It should be possible to combine seamless spatial information from different sources across
Europe and share it with many users and applications.
• It should be possible for information collected at one level/scale to be shared with all
levels/scales; detailed for thorough investigations, general for strategic purposes.
• Geographic information needed for good governance at all levels should be readily and
transparently available.
• Easy to find what geographic information is available, how it can be used to meet a particular need,
and under which conditions it can be acquired and used.
Infrastructure for Stadard Information in Europe (INSPIRE)
25. Principles behind prescriptions
D. INSPIRE, SEIS, Copernicus and GEOSS principles
• Managed as close as possible to its source.
• Collected once and shared with others for many purposes.
• Readily available to easily fulfil reporting obligations.
• Easily accessible to all users.
• Accessible to enable comparisons at the appropriate geographical scale and the participation of
citizens.
• Fully available to the general public and at national level in the relevant national language(s).
• Supported through common, free, open software standards.
Shared Environmental Information System (SEIS)
26. Principles behind prescriptions
D. INSPIRE, SEIS, Copernicus and GEOSS principles
The vast majority of data/information delivered by Copernicus is made available and accessible to
any citizen, and any organisation around the world on a free, full, and open basis.
Copernicus
27. Principles behind prescriptions
D. INSPIRE, SEIS, Copernicus and GEOSS principles
• data, metadata and products will be shared as Open Data by default, by making them available as
part of the GEOSS Data Collection of Open Resources for Everyone (Data-CORE) without charge or
restrictions on reuse, subject to the conditions of registration and attribution when the data are
reused;
• where international instruments, national policies or legislation preclude the sharing of data as
Open Data, data should be made available with minimal restrictions on use and at no more than
the cost of reproduction and distribution;
• all shared data, products and metadata will be made available with minimum time delay.
Group on Earth Observation System of Systems (GEOSS)
28. Implications for Contracting Parties
• If there are adequate competencies, data must be
handled as close as possible to its source, following
the principle to do not duplicate data.
• Data shall be made available with the minimum time
delay at no cost.
• Data created by UNEP-MAP, Regional Activity Centers,
and MAP components should be as open as possible
(fully available to the general public), where for
“open” we intend free, accessible without further
barriers and covered by an open license.
• Data should be given with location (or
Latitude/Longitude coordinates) whenever possible
for environmental data.
Common implications for all the data:
29. Implications for Contracting Parties
• In the case of data owned by the Contracting Parties
or third parties, eventual barriers or limitations to
data sharing must be verified to assess their
compliance with the data policy before any action to
be pursued on data. Also, intellectual property rights,
use or reuse conditions, confidentiality, and data
quality statement must be verified.
• If data is “restricted” or strategic for the Contracting
Party it will be not opened to the large public but it
will be shared only among appropriate (authorized)
users.
• When necessary, confidential, or sensitive data could
be reclassified or aggregated by cooperating with
INFO/RAC in order to open the dataset.
• If data is matter of scientific publication or it is
involved in consortium contract or patent
registration, it could be subjected to embargo. To
formalize the embargo (which should not last less
than 24 months, ideally) the partner must motivate
the request (for embargo) and in the metadata
embargo duration should be explicitly stated.
Particular implications for sensitive data:
30. Implications for Contracting Parties
• In particular, restrictions to put attention on are:
o Binding rules
o International treaties
o National legislation
o Personal data (protection of)
o Statistical confidentiality
o Protection of intellectual property rights
o Protection of national security
o Defense purposes
o Public security
Particular implications for sensitive data:
31. Implications for Contracting Parties
• In particular, restrictions to put attention on are:
o Binding rules
o International treaties
o National legislation
o Personal data (protection of)
o Statistical confidentiality
o Protection of intellectual property rights
o Protection of national security
o Defense purposes
o Public security
Particular implications for sensitive data:
32. Thanks for the Attention
Questions
?
Annalisa.Minelli@info-rac.org
You can reuse this
work upon attribution