The CODATA OECD Global Science Forum Project on Business Models for Sustainable Repositories

Transcription

1 Phoenix Workshop: Data Resource ustainability and Funding PAG 2017 Conference an Diego, A 16 January 2016 The OECD Global cience Forum Project on Business Models for ustainable Repositories Dr imon Hodson ExecuCve Director,

2 I Prospectus: h2ps://doi.org/ /zenodo Principles, Policies and PracDce Capacity Building FronDers of Data cience Data cience Journal IDW 2016, ept, Denver, CO.

3 The Challenge: Business Models for ustainable Data Repositories! Research increasingly relies on digital repositories, curated databases and services over data.! Research funder policies increasingly mandate data stewardship of data produced by funded projects.! ignificant important policy developments in recent years:! OECD Principles and Guidelines to EC Horizon 2020 Policy.! Increasing need for data repositories and data stewardship.! Increasing volume presents a challenge.! Requirements for stewardship present a greater challenge.! ustaining digital data infrastructure is a major issue for science policy!! Genuine concern that current funding models will prove inelascc and not meet the growing requirements concern on the part of repositories and funders.! Important to demonstrate the value proposidon of data repositories / data services.! ustainability is not just about whether something is funded, but how it is funded: what are the most effecdve and sustainable mechanisms for funding?

4 OECD Global cience Forum Project: Business Models for ustainable Data Repositories! RelaCvely liyle work has been done on the economics and business models of data infrastructure.! Blue Ribbon Task Group Report on ustainable Digital PreservaCon: hyp://br[.sdsc.edu/biblio/brtf_final_report.pdf " Need to understand value proposidon for communides.! ustaining Domain Repositories for Digital Data: A White Paper (ICPR): hyp://datacommunity.icpsr.umich.edu/sites/default/files/ WhitePaper_ICPR_DRDD_ pdf " Need to understand how repositories are funded.! OECD Project builds on previous work of RDA-WD Interest Group: Income treams for Data Repositories : hyp://dx.doi.org/ /zenodo.46693

5 OECD Global cience Forum Project: Business Models for ustainable Data Repositories Central quesdons:! How are data repositories currently funded?! What innovacve income streams are available to data repositories?! What means of opcmising costs are available?! How do income streams match willingness/ability to pay of various stakeholders?! How do income streams/willingness to pay fit together into a sustainable business model? Deliverable:! Report summarizing findings and containing policy recommendacons for OECD member states to promote sustainable business models for data infrastructures.

6 OECD Global cience Forum Project: Business Models for ustainable Data Repositories! Co-Chairs of OECD Project: Allen Dearry, NIH; Ingrid Dillo, DAN; imon Hodson,.! Consultant and writer: Paul hlir, previously NA.! Economics consultant: John Houghton, author of many studies of economics of data infrastructure. Project Programme! urvey: Extend landscape survey of current repositories, their funding and sustainability, May-Oct 2016 (47 repos).! Workshop 1: Focus group on emerging income streams, WOT analyses and on cost restraint, workshop Nov 2016.! Analysis: Micro and macro economic analysis of business models, Nov 2016-March 2017.! Workshop 2: Test business models with stakeholder groups, workshop March 2017.! Report and RecommendaDons: Policy recommendacons based on business model opcons, April-June 2017.

7 Typology of Revenue ources Research Project Funder Research Performing OrganisaDon Researcher / PI / Project (tructural) Infrastructure Funder Data Centre / Archive Private ContracDng 1) tructural (central contract) 2) HosDng upport (indirect or direct support through insdtudonal hosdng) 3) Annual Contract (from deposidng insdtudon) 4) Data Deposit Fee (may be paid by researcher, RPO or publisher; may originate with funder) 5) Access Charge (for the data or for value-adding services) 6) R&D Projects (to develop infrastructure or value-adding services) 7) Private ContracDng (services to pardes other than core funder)

8 Repositories in which countries? Charts by John Houghton for OECD GF Project

9 Repositories for which subjects? Charts by John Houghton for OECD GF Project

10 Range of Funding ources Charts by John Houghton for OECD GF Project

11 Range of Funding ources Charts by John Houghton for OECD GF Project

12 InnovaDons in the Funding of Data Repositories! A large number of repos are accvely looking at alternacve funding sources (72% of survey).! Witnessing InnovaCon, changes in funding / business models! Archaeology Data ervice (AD), K: structural funding to deposit fee! Dryad, : deposit fees.! The Arabidopsis InformaDon Resource (TAIR), : project funding to subscripcon charges! CCDC, K: raw data is open and subsidised by charges for value added data and sokware tools! DAN (Data Archive and Network ervices), Netherlands: diversificacon of income, parccular services to insctucons, contracts for parccular research projects! ICPR, Inter-university ConsorDum for PoliDcal and ocial Research, : balancing open and membership content! Private, for-profit service providers:! Fighare: for journals and research insctucons! Mendeley: for Elsevier journals

13 Building Evidence: Witness tatements at cidatacon 2016 cidatacon 2016, part of InternaDonal Data Week, Denver, eptember! ession on economics and governance: broad themes! ession containing witness statements from nine repositories! Digital Repository of Ireland, DAN, CCDC, Flywheel, tdar (the Digital Archaeological Record), Protein Data Bank (PDB), Dryad, Inter-university ConsorCum for PoliCcal and ocial Research (ICPR), TAIR! hyp://wiki.codata.org/ cidatacon_ession:_witness_tatements_on_repository_business_models

14 InnovaDve Funding Models, WOT Analyses and Cost OpDmisaDon Workshop 1, 3-4 November in Paris! What innovacve income streams are available? What innovacve streams are being explored by repositories? What examples are there from other sectors?! WOT Analyses: what are the pros and cons of different funding models.! What opportunices are there for cost reduccon? Where can technology, partnerships, prioriczacon, help restrain the cost of data stewardship?! AYendees:! Members of the expert group! Invitees: commercial repository services, European inicacves like EOC, EDAT, etc.

15 Broad Picture: Business Models for ustainable Data Repositories! tructural funding suits many repositories although oken supplemented from other sources and some concerns expressed about flexibility and adaptability.! Many repositories are interested in charging for value-added services.! Access charges, subscripcons and membership can be micgated (e.g. brief embargoes, charging for access to value added services and tools, not the raw data).! ome community push-back and concerns.! Data deposit fees are being explored by a small number of repositories.! Data deposit fees may gain stakeholder acceptance because of similarity to APCs! Can be scalable and effeccve for data from project funding! Concern about administracve overheads and that encourage cheaper, lower levels of curacon.! Many data repositories value parccipacon in research and R&D projects.! Frequent addicon to overall income, brings skills and R&D.! ome are highly / vulnerably dependent on this income and overheads need to be considered.! Emerging demand for contracdng repository and archive services for research insctucons.! Open membership models and micropayments being piloted and explored

16 Economic Analysis and Acceptance TesDng with takeholders Workshop 2, March in Brussels! Consultant to carry out in depth economic analysis of business models to be tested with stakeholders (funders, repository managers, users) in workshop 2 QuesDons:! How do income streams match willingness/ability to pay of various stakeholders?! How do income streams/willingness to pay fit together into a sustainable business model?! Are some ways of paying more suited to parccular repository missions and levels of curacon?! What are the (unintended) consequences of parccular payment regimes? E.g. pay to access, pay to deposit?

17 Economic Analysis and Business Models! Does the business model provide a source of revenue that scales with demand and costs? Do some funding models scale more easily than others?! What features or mechanisms of the model result in it being responsive or not to requirements?! Which stakeholders benefit from the data being freely available and how?! What effect are different ways of funding data repositories likely to have upon produccon and consumpcon behaviours?! What are the implicacons of the various funding models on cost constraint? What are the mechanisms in the revenue model that encourage cost opcmisacon?! Are some ways of funding more suitable for certain types of repositories than others? Are there opcmal mixes of funding sources for various types of repositories? (ee typology of repository, levels of curadon )

18 Where should research data go? Homogenous data colleccons essencal for research Earth observacon data; GeneCc data; ocial science survey data NaConal and internaconal data archives ignificant data outputs of publicly funded research ignificant data outputs from funded projects; Raw and analysed experimental data NaConal or insctuconal data archives; data papers Data underpinning research publicacons Raw and analysed data for reproducibility (evidence); Data behind the graph Dedicated data archives (e.g. Dryad)

19 Typology of Data CollecDons Research data collecdons! are the products of one or more focused research projects and typically contain data that are subject to limited processing or curacon. They may or may not conform to community standards, such as standards for le formats, metadata structure, and content access policies. Quite oken, applicable standards may be nonexistent or rudimentary because the data types are novel and the size of the user community small. Research colleccons may vary greatly in size but are intended to serve a speci c group, oken limited to immediate parccipants. There may be no intencon to preserve the colleccon beyond the end of a project. One reason for this is funding. These colleccons are supported by relacvely small budgets, oken through research grants funding a specific project. Resource or community data collecdons! serve a single science or engineering community. These digital colleccons oken establish community-level standards either by seleccng from among preexiscng standards or by bringing the community together to develop new standards where they are absent or inadequate. The budgets for resource or community data colleccons are intermediate in size and generally are provided through direct funding from agencies. Because of changes in agency priorices, it is oken difficult to anccipate how long a resource or community data colleccon will be maintained. Reference data collecdons! are intended to serve large segments of the sciencfic and educacon community. CharacterisCc features of this category of digital colleccons are a broad scope and a diverse set of user communices including sciencsts, students, and educators from a wide variety of disciplinary, insctuconal, and geographical sesngs. In these circumstances, conformance to robust, well-established, and comprehensive standards is essencal, and the seleccon of standards by reference colleccons oken has the effect of creacng a universal standard. Budgets supporcng reference colleccons are oken large, refleccng the scope of the colleccon and breadth of impact. Typically, the budgets come from mulcple sources and are in the form of direct, long-term support, and the expectacon is that these colleccons will be maintained indefinitely.

20 ome Criteria AffecDng Business Model! ize of user base?! NaConal or internaconal user base?! MulC-disciplinary or single discipline?! Research community or wider society, government?! Origin of data?! Ongoing data gathering exercise?! Research project data?! Level of curacon: e.g. automated ingest, low curacon required, very high levels of manual curacon required! Legal or policy context favouring or disadvantaging a parccular model (e.g. AD)! Why do some business / funding models work for some repositories?

21 OECD Global cience Forum Project: Business Models for ustainable Data Repositories tructure of the Final Report (final draj for review by GF, June 2017) 1. Why is this important? a) The benefit and value for data stewardship b) The potencal vulnerability of data infrastructure 2. Background and context to the project 3. Landscape of funding for data stewardship 4. InnovaCve income streams 5. OpportuniCes for cost restraint 6. ustainable business models a) Pros and cons, WOT, economic analysis b) Results of stakeholder tescng 7. Conclusion and policy recommendacons

22 Thank you for your ayencon! lide Credits also to: Ingrid Dillo, John Houghton imon Hodson ExecuCve Director hyp://lists.codata.org/mailman/liscnfo/codata-internaconal_lists.codata.org Tel (Office): Tel (Cell): (IC Committee on Data for cience and Technology), 5 rue Auguste Vacquerie, Paris, FRANCE

23 Project cope: DefiniDon of Repository! Repositories preserve, manage, and provide access to many types of digital materials in a variety of formats. Materials in online repositories are curated to enable search, discovery, and reuse. There must be sufficient control for the digital material to be authencc, reliable, accessible and usable on a concnuing basis.! DefiniCon from CARAI RDM Glossary hyp://dicconary.casrai.org/repository ; see also hyp:// ome repositories curate and provide access. Others provide a range of services over the data. The important factor is providing the stewardship funccon. Aggregator services that do not provide curate the data are out of scope.

24 I Why Open cience / FAIR Data? Good sciencfic praccce depends on communicacng the evidence. Boulton: to fail to communicate the data that supports sciencfic assercons is malpraccce. Open research data are essencal for reproducibility, self-correccon. Research data produced by publicly funded research are a public asset. Research data oken have considerable potencal for reuse, reinterpretacon, use in different studies. Open data foster innovacon and accelerate sciencfic discovery through reuse of data. Open data praccces have transformed certain areas of research. Research data should be open by default; but there are good reasons for restriccng some data. Intelligently Open Data to FAIR Data. As open as possible, as closed as necessary. Research data is an asset to research insctucons and universices. EssenCal role of RDM in universices in the data ecosystem.

25 Policy Push for Open Research Data The three Bs (Budapest, Berlin and Bethesda) and Open Access, OECD Principles and Guidelines on Access to Research Data, 2007 K Funder Data Policies, from 2001, but accelerates from 2009 NF Data Management Plan Requirements, 2010 Royal ociety Report cience as an Open Enterprise, 2012 OTP Memo Increasing Access to the Results of Federally Funded ciencfic Research, Feb 2013 G8 cience Ministers tatement, June 2013 G8 Open Data Charter and Technical Appendix, June 2013 EC H2020 Open Data Policy Pilot, 2014 cience InternaConal Accord on Open Data in a Big Data World, Dec 2015: hyp://bit.ly/opendata-bigdata

26 Benefits of Open Data: some examples from GEO! Barbara Ryan, Director of ecretariat GEO, TED-X Talk Barcelona! In 2008 Government was convinced to make Landsat Data openly available, for free.! nder charging, the highest number of downloads was 53 scenes per day.! Now over 5700 scenes per day are downloaded.! panish deforestacon research: under the charging regime data access alone would have cost 260M! produced a White Paper on the Value of Data haring for the GEO-XII Plenary: h2p://dx.doi.org/ /zenodo hyps://

27 I The Value of Open Data haring! Report by for GEO, the Group on Earth ObservaCon.! Provides a concise, accessible, high level synthesis of key arguments and evidence of the benefits and value of open data sharing.! ParCcular, but not exclusive, reference to Earth ObservaCon data.! Benefits in the areas of:! Economic Benefits! ocial Welfare Benefits! Research and InnovaCon OpportuniCes! EducaCon! Governance! Available at hyp://dx.doi.org/ /zenodo.33830! GEO DWG is building on this work with further examples: would be valuable to work with this community.

28 Economic Benefits of Data haring: Landat! 2006 tudy escmated the loss in case of a data gap as equivalent to $935 M.! 2011 tudy escmated benefits of landsat-sourced informacon for agriculture as $858 M just for the state of Iowa.! 2015 tudy escmated worldwide economic benefit of $2.19 BN.! EsCmated benefit in of $1.8 BN.! Valuing GeospaCal InformaCon: sing the ConCngent ValuaCon Method to EsCmate the Economic Benefits of Landsat atellite Imagery: hyp://dx.doi.org/ /per (Paywall Irony )! Open data and open data infrastructure has a significant economic benefit.

29 The Value and Impact of Data haring and Cura8on: A synthesis of three recent studies of K research data centres (Neil Beagrie and John Houghton) h2p://repository.jisc.ac.uk/5568/1/idf308_-_digital_infrastructure_direcdons_report%2c_jan14_v1-04.pdf

30 Data RevoluCon: how can we improve with open data?! GODAN-ODI Report: improving agriculture, food and nutricon with open data.! Although the amount of data openly available is constantly increasing, there are s8ll challenges related to data management, licensing, interoperability and exploita8on. There is a need to evolve policies, prac8ces and ethics around closed, shared, and open data.! Enabling more efficient and effecdve decision making > lowers cost of accessing informacon and underpins tools that farmers themselves can use.! Fostering innovadon to benefit everyone > an opportunity that must not be missed for creacng new businesses and jobs in new datapowered innovacon ecosystems.! Driving organisadonal and sector change through transparency > open data is essencal to understanding complex systems, intervencons, targets, change.! Availability is not enough > essencal that the data be interoperable and machine-readable.! Problem oriented and solucon-based data strategies.! Develop infrastructure and human capacity.

31 se cases Improving crop varieties with open data on breeding trials: AgTrials Cultivar testing is an important means of improving crop varieties. A wide range of trials are taking place on sites all over the world, addressing issues such as drought tolerance, heat stress, and soil management. However, almost all of the data generated sometimes lost completely due to bad data management. By compiling data from agronomic and plant breeding trials and making it open, the Global Agricultural Trial Repository (AgTrials)38 hosted by a CGIAR Research Programme base to inform ongoing, collaborative research, while eliminating unnecessary and Africa region. The models are used to project the local impacts of climate change, and 39