Transcript Open access - digital

Open Science: What, why, how?
Remedios Melero
Consejo Superior de Investigaciones Científicas (CSIC),
FOSTER partner
Obra licenciada con Creative Commons By 4.0 internacional
https://www.fosteropenscience.eu/
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Diagram of Foster’s Content Classification
https://www.fosteropenscience.eu/
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Open science is beyond open access
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Arbeck (2013).
http://commons.wikimedia.org/wiki/File:Open_Science_Does_Not_Equal_Open_Access.svg
Significado de la Open Science
“Open Science (OS) offers researchers tools and workflows for transparency,
reproducibility, dissemination and transfer of new knowledge”
“The conduction of science in a way that others can collaborate and
contribute, where research data, lab notes and other research processes
are freely available, with terms that allow reuse, redistribution and
reproduction of the research. ( Open science,
http://en.wikipedia.org/wiki/Open_science)
“Open science is the idea that scientific
knowledge of all kinds
should be openly shared as early as is practical in the discovery
process.”
(Michael Nielsen, http://openscienceasap.org/open-science/ )
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Principios de la Open Science
Open Methodology (Métodos, procesos, documentos relevantes)
Open Source (Soft- y Hardware)
Open Data ( datos reutilizables)
Open Access to scholarly outputs (acceso gratis y libre)
Open Peer Review (transparencia en la evaluación y en los criterios de calidad)
Open Educational Resources (MOOCs, OERs)
http://openscienceasap.org/open-science/
Open access…( término definido por primera vez en la
Declaración de Budapest, febrero 2002)
“Los recursos en acceso abierto son digitales, online, libres de
cargas económicas, libres de la mayor parte de restricciones
debidas a los derechos de explotación” (Peter Suber)
Objetos digitales de acceso abierto:
• Acceso gratuito online (libre de barreras económicas)
• Eliminan ± restricciones de copyright (permite la reutilización
de acuerdo a los permisos o licencias que se establezcan)
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Consecuencias/beneficios del acceso abierto
Visibilidad
Rompe
barreras
Retorno de
inversión
“Impacto”
Tendencias
Responsabilidad
Impacto social
Data lifecycle
https://www.jisc.ac.uk/sites/default/files/research_data_life_diagram.jpg
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Open data must be accessible, useable, assessable
and intelligible ( extracted from Science as an Open
Enterprise, 2012 )
Accessible
Data must be located in such a manner that it can readily be found and in a form that
can be used.
Useable
In a format where others can use the data or information. Data should be able to be
reused, often for different purposes, and therefore will require proper background
information and metadata.
Assessable
In a state in which judgments can be made as to the data or information’s reliability.
Intelligible
Comprehensive for those who wish to scrutinise something.
FAIR Data Principles:
•
•
•
•
Findable
Accessible
Interoperable
Re-usable
Data can be cited….
Identification of datasets favours their use and citation
Australian National Data Service. http://www.ands.org.au/cite-data/index.html
http://www.dcc.ac.uk/sites/default/files/documents/data-forum/documents/events/dcc2010/posters/Contribution165wilson.pdf
DataCite: Locate, Identify, Cite data
https://www.datacite.org/
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http://www.re3data.org/
http://repository.jisc.ac.uk/6269/10/final-KE-Report-V5.1-20JAN2016.pdf
Europa vs Open science…..
http://zenodo.org/
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http://www.openaire.eu/
https://www.eudat.eu/sites/default/files/The%20B2%20SERVICE%20SUITE.pdf
http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilo
t/h2020-hi-oa-pilot-guide_en.pdf
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Research Data Pilot in H2020
A novelty in Horizon 2020 is the Open
Research Data Pilot which aims to improve
and maximise access to and re-use of
research data generated by projects. The
legal requirements for projects
participating in this pilot are contained in
the optional article 29.3 of the Model
Grant Agreement.
http://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_p
ilot/h2020-hi-oa-data-mgt_en.pdf
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For the 2016-2017 Work Programme, the areas of Horizon 2020 participating in the
Open Research Data Pilot are:
• Future & Emerging Technologies
• Research infrastructures
• Leadership in enabling & industrial technologies – Information & Communication
Technologies
• Nanotechnologies, Advanced Materials, Advanced Manufacturing & Processing, &
Biotechnology – 'nanosafety' & 'modelling' topics
• Societal Challenge – Food security, sustainable agriculture & forestry, marine &
maritime & inland water research & the bioeconomy - selected topics as specified
in the work programme
• Societal Challenge – Climate Action, Environment, Resource Efficiency & Raw
Materials – except raw materials
• Societal Challenge – Europe in a changing world – inclusive, innovative &
reflective societies
• Science with & for Society
• Cross-cutting activities – focus areas – part Smart & Sustainable Cities
Projects in other areas can participate on a voluntary basis
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Opting out partially or entirely from the Pilot on Open Research Data
Projects can opt out at any stage if:
• Participation is incompatible with the Horizon 2020 obligation to protect
• Results that can reasonably be expected to be commercially or industrially exploited
• Participation is incompatible with the need for confidentiality in connection with
security issues
• Participation is incompatible with rules on protecting personal data
• Participation would mean that the project's main aim might not be achieved
• The project will not generate / collect any research data
• There are other legitimate reasons not to take part in the Pilot (at proposal
stage - free text box provided).
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In a research context, examples of data include statistics, results of experiments,
measurements, observations resulting from fieldwork, survey results, interview
recordings and images. The focus is on research data that is available in digital
form.
Types of data covered by the Open Research Data Pilot:
1. The data, including associated metadata (i.e. metadata describing the
research data deposited), needed to validate the results presented in
scientific publications as soon as possible ("underlying data")
2. Other data (for instance curated data not directly attributable to a
publication, or raw data), including associated metadata, as specified and
within the deadlines laid down in the data management plan that is,
according to the individual judgement by each project
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References to research data management are included in Article 29.3 of the Model
Grant Agreement (article applied to all projects participating in the Pilot on Open
Research Data in Horizon 2020).
29.3 Open access to research data
[OPTION for actions participating in the open Research Data Pilot: Regarding the
digital research data generated in the action (‘data’), the beneficiaries must:
(a) deposit in a research data repository and take measures to make it possible for
third parties to access, mine, exploit, reproduce and disseminate — free of charge
for any user — the following:
(i) the data, including associated metadata, needed to validate the results
presented in scientific publications as soon as possible;
(ii) other data, including associated metadata, as specified and within the deadlines
laid down in the ‘data management plan’ (see Annex 1);
(b) provide information — via the repository — about tools and instruments at the
disposal of the beneficiaries and necessary for validating the results (and — where
possible — provide the tools and instruments themselves).
https://ec.europa.eu/research/participants/data/ref/h2020/grants_manual/hi/oa_pilot/h
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2020-hi-oa-pilot-guide_en.pdf
Projects participating in the pilot will be required to develop a
Data Management plan (DMP), in which they will specify what
data will be open.
• The Commission does NOT require applicants to
submit a DMP at the proposal stage.
• A DMP is therefore NOT part of the evaluation.
• DMPs are a deliverable for those participating in the
pilot (within first 6 months, at midterm and at the end
of the project- at least)
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From vision to action
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Draft European Open Science
Agenda. 26 February 2016
Based on 5 policy actions:
•
•
•
•
•
Foster Open Science
Remove barriers to Open Science
Develop research infrastructures for Open Science
Mainstream Open Access to research results
Embed Open Science in Society
https://ec.europa.eu/research/openscience/pdf/draft_european_open_science_agenda.pdf
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Draft European Open Science Agenda. 26 February 2016
Some actions…
• Reward researchers engaged in Open Science activities (career development)
• Allow research funders to provide specific incentives for 'collaborative science‘
including societal actors and citizen science
• Improve expertise and guidance (In open science)
• Implement data-sharing principles (e.g. G8 principles / FAIR)
Some implementations….
•
•
•
•
•
•
•
Recognize new professions e.g. Establish a professorship on Openness, on Big data
management, data mining etc.
Introduce openness as criterion for receiving research funding
Analyse current competency levels (research organisations)
Adapt university curricula to new needs
Pilot a EU Certificate of Open Research
Create incentives for skill transfer in data analytics and cloud technology for research
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This document is a living document reflecting the
present state of open science evolution. It is based on
the input of many participating experts and
stakeholders of the Amsterdam Conference ‘Open
Science – From Vision to Action’, hosted by the
Netherlands’ EU Presidency on 4 and 5 April 2016.
Formulated to reach two important pan-European goals for 2020:
1. Full open access for all scientific publications
2. A fundamentally new approach towards optimal reuse of research data
To reach these goals by 2020 we need flanking policy:
• New assessment, reward and evaluation systems
• Alignment of policies and exchange of best practices
http://english.eu2016.nl/documents/reports/2016/04/04/amsterdam-call-for-action-on39
open-science
Twelve actions grouped around the five cuttig themes that follow the
structure of the European Open Science Agenda proposed by the EC
Removing barriers to open science
1. Change assessment, evaluation and reward systems in science
2. Facilitate text and data mining of content
3. Improve insight into IPR and issues such as privacy
4. Create transparency on the costs and conditions of academic communication
Developing research infrastructures
5. Introduce FAIR and secure data principles
6. Set up common e-infrastructures
Fostering and creating incentives for open science
7. Adopt open access principles
8. Stimulate new publishing models for knowledge transfer
9. Stimulate evidence-based research on innovations in open science
Mainstreaming and further promoting open science policies
10. Develop, implement, monitor and refine open access plans
Stimulating and embedding open science in science and society
11. Involve researchers and new users in open science
12. Encourage stakeholders to share expertise and information on open science
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Datos sobre autores vs acceso abierto y datos
Researchers’ green open access practice: a cross-disciplinary analysis. Spezi et
al., 2013 (https://dspace.lboro.ac.uk/dspace-jspui/handle/2134/12324).
Some results from the EC-funded Publishing and the Ecology of European Research (PEER)
project (http://www.peerproject.eu/)
Motivaciones para el depósito por tipo de repositorio
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Quién hace el depósito en repositorios institucionales
Quién hace el depósito en repositorios temáticos
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La disciplina importa….
UNESCO (2012), Policy Guidelines for the Development and Promotion of Open Access, UNESCO
Publishing, and Björk et al. (2010), “Open Access to the scientific journal literature: Situation
2009”, PloS ONE, Vol. 5, No. 6.
De dónde obtiene el trabajo. Preliminary analysis of OECD NESTI Pilot Survey
of Scientific Authors 2014-15. Note: NK = not known.
http://www.tandfonline.com/page/openaccess/opensurvey/2014
Ventajas del OA
Reasons for self-archiving
Factors not to upload your
article
Encuesta de la EUA entre universidades europeas (106 univ. de 30 países hecha en 2014).
Standard practice, increase impact and
public benefit
http://exchanges.wiley.com/blog/wp-content/uploads/2014/11/Researcher-Data-InsightsInfographic-FINAL-REVISED-2.jpg
https://www.openaire.eu/intro-researchers
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http://learn-rdm.eu
The purpose of LEARN is to take the LERU Roadmap for Research Data produced by
the League of European Research Universities (LERU) and to develop this in order to
build a coordinated e-infrastructure across Europe and beyond. LEARN will deliver:
• a model Research Data Management (RDM) policy;
• a Toolkit to support implementation, and;
• an Executive Briefing in five core languages so as to ensure wide outreach.
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Project Outputs
1) An analytic rubric to standardize the review of data management plans as a
means to inform targeted expansion or development of research data
services at academic libraries;
2) A study utilizing the rubric that presents the results of data management plan
analyses at five universities.
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http://edison-project.eu/
Data Scientist is a complex
profession
..”data scientists range from pure e-Science driven by research communities, to applications
of Data Science Professionals in Public Institutions”
“future Data Scientists must posses knowledge (and obtain competencies and skills) in data
mining and analytics, information visualisation and communication, as well as in statistics,
engineering and computer science, and acquire experiences in the specific research or
industry domain of their future work and specialisation.
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www.indeed.es
Trends….
www.indeed.com
https://www.domo.com/learn/the-world-needs-datascientists
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http://datasupport.researchdata.nl/en/start-de-cursus/ii-planfase/datamanagementplanning/
https://youtu.be/gYDb-GP1CA4
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¡Gracias!
Gràcies!
[email protected]
Mas información:
http://www.fosteropenscience.eu/
https://www.openaire.eu/opendatapilot
http://www.consorciomadrono.es/pagoda/index2.php
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