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Sarah Jones RDM from a disciplinary perspective

Sarah Jones RDM from a disciplinary perspective

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Sarah Jones RDM from a disciplinary perspective

  1. 1. Research Data Management from a disciplinary perspective S a rah Jones D i g i tal Curat i o n Cent re s a rah .jones@glasgow. a c . u k Twi t ter : @sjDCC S téphane Goldste i n Research I n format i o n Netwo r k s tephane.go l d stein@re s e a rc h i nfonet . o rg Twi t ter : @stephgold7
  2. 2. Disclaimer Practice varies greatly by discipline and sub‐discipline so it’s hard to generalise Apologies for any sweeping statements and groupings that don’t fit your model Image credit: Sweep by Judy Van der Velden CC‐BY‐NC‐ND www.flickr.com/photos/judy‐van‐der‐velden/6757403261
  3. 3. Case studies on disciplinary practice  RIN Information Seeking and Sharing Behaviour www.rin.ac.uk/our‐work/using‐and‐accessing‐information‐resources – Life sciences – Humanities – Physical sciences  RIN Open Science Case studies www.rin.ac.uk/our‐work/data‐management‐and‐curation/open‐science‐case‐studies  SCARP case studies www.dcc.ac.uk/resources/case‐studies/scarp  Knowledge Exchange Incentives and motivations for sharing research data (forthcoming)  RLUK research data typology (more from Stephane)
  4. 4. Groups and disciplines  Arts & Humanities – Creative arts, languages, philosophy, archaeology…  Social Science – Economics, history, politics, business, psychology...  Sciences & Engineering – Physics, astronomy, earth sciences, computing…  Life Sciences – Biology, ecology, medical and veterinary science…
  5. 5. Arts & Humanities  Outputs may not be termed ‘data’ e.g. sketches, writing, performance, artefacts, ‘work’  Focus on literary outputs & manuscripts in some disciplines  More use of standard tools e.g. Word, Excel – less likely to adapt technologies to fit  Arguably lower awareness and uptake of RDM overall
  6. 6. Creative Arts  Several RDM projects in the creative arts e.g. Kultivate, KAPTUR, VADS4R, CAiRO training...  Resistance to term ‘data’ – too scientific  Importance of personal websites for profile as work is also conducted outside of academia  Visual Arts Data Service ‐ www.vads.ac.uk  Institutional repositories at arts schools accept a broader range of outputs and display content more visually to fill the void e.g. http://research.gold.ac.uk
  7. 7. Sonic Arts Research Unit  Collaboration with IR as a result of losing data  Tension between providing access in a visual / usable way and preserving data  Still use soundcloud and personal websites for access, but these link to ‘master’ copy of data held in IR for preservation www.dcc.ac.uk/resources/developing‐rdm‐services/repository‐radar
  8. 8. Digital Humanities  Intentional creation of resources rather than just data as by‐product of research process  More use of standards e.g. XML & TEI in language resources, image standards and capture quality for digitisation, Dublin Core metadata…  Often include technical experts in project team  Links with cultural heritage collections  Negotiating copyright often a major issue  Sustainability a big challenge
  9. 9. Mapping Edinburgh’s Social History Historical maps overlaid these with all kinds of open data to chart how the town has changed through time  Uses open source tools  Allows you to overlay maps  Picks up on common themes www.mesh.ed.ac.uk
  10. 10. Social Sciences  Greater awareness and acceptance of RDM by community  Methodology is as much a factor in determining difference as discipline  Nature of data often poses challenges for sharing  Lots of reuse of large survey data  Established metadata standards e.g. Data Documentation Initiative (DDI)  Strong international data centre infrastructure
  11. 11. Public health  Ethics predominant concern – How to negotiate consent – How to store, transfer & handle data securely – How to anonymise and share data  Data integration / linking and curation of longitudinal studies is major concern as data added to over decades  Need for data havens to help control access to data – role for unis e.g. Grampian Data Safe Haven  UK Data Service ‐ http://ukdataservice.ac.uk
  12. 12. Twenty‐07: Public health study  Longitudinal study following 4510 people from West of Scotland over 20 years to investigate the reasons for differences in health  Undertook interviews, questionnaires, physical measurements, blood samples etc  Strict access controls and guidelines for data collection  Data managed within the MRC Social and Public Health Sciences Unit and accessible under a data sharing agreement ‐ http://2007study.sphsu.mrc.ac.uk/Revised‐Data‐Sharing‐Policy‐has‐ been‐launched.html
  13. 13. Life Sciences  Funders arguably more demanding in terms of data sharing policy  Sharing can be problematic / resisted given the nature of the data, fear of misuse or loss of control over IPR  Data sharing agreements and access committees more common  Data integration & mining key drivers  Research is well‐resourced so greater capacity to fund local solutions and tools for RDM during projects
  14. 14. Genetics  Vast quantities of data and rapid growth – DNA sequence data is doubling every 6‐8 months  Well established public databases for gene sequences e.g. GenBank www.ncbi.nlm.nih.gov/genbank – However even this is on short‐term project funding!  Need accession number to publish so driver for sharing and established workflow  European Data Infrastructure projects too e.g. ELIXIR
  15. 15. Neuroscience  Large data volumes due to use of medical imaging  Moving towards larger cohort studies integrating wider range of data types, which strains the balance with ethical requirements around personal data  Costs of data gathering and advances in analysis technology are making field more data intensive ‐ computational methods  Small interdisciplinary teams provide the human infrastructure for RDM, but historically low funder investment in data management at lab level  Disciplinary archives are immature, and has encouraged tendency for labs to treat longitudinal datasets as intellectual capital
  16. 16. OMERO – Open Microscopy Environment  Monash e‐Research Centre helps groups to adopt (and if needed adapt) existing technological solutions  Partnered a research group to implement OMERO, a secure central repository to help researchers organise, analyze and share images  Resulting tool more sustainable as tailored to specific community need www.dcc.ac.uk/resources/developing‐rdm‐services/improving‐rdm‐monash
  17. 17. Science & Engineering  Large scale can mean RDM is built in as standard and sharing part of workflow e.g. facilities science  Often early adopters and advocates of new technologies e.g. the Grid, wikis & Arxiv in particle physics  Archiving established in some cases as data can’t be recreated e.g. NERC data centres for Earth Sciences  Commercial sensitivities can place restrictions on sharing in some fields Industry partners
  18. 18. Mechanical Engineering  Several RDM projects at Bath e.g. ERIM, REDm‐MED  Concept of repository well established in industrial engineering – Product Lifecycle Management (PLM) systems  Preservation issues as data is challenging e.g. CAD files  Less information sharing than other disciplines – Commercial sensitivities preclude sharing – Consultancy‐style research can lead to internal‐only results – Data generated from private systems, so less applicable to others
  19. 19. Crystallography  X‐ray examinations, images and videos of crystal structures, chemical crystallography diffraction images  Established metadata standards e.g. Crystallographic Information Framework (CIF)  Advocates of open science and use of related tools  UsefulChem ‐ http://usefulchem.wikispaces.com  LabTrove ‐ www.labtrove.org  eCrystals Archive and Crystallography Open Database (COD)  National Crystallography Service ‐ www.ncs.ac.uk
  20. 20. Astronomy  Established data standards (e.g. FITS and NOA) maintained by community  Access to facilities requires the deposit of raw data, although this can be embargoed  International data centres e.g. Sloan Digital Sky Survey ‐ www.sdss.org  Large volumes of data so transfer can be difficult  Few IPR issues compared to other disciplines  Data products are not always shared
  21. 21. Galaxy Zoo  Citizen Science project started to classify a million galaxies imaged by the Sloan Digital Sky Survey  Over 50 million classifications in the first year, contributed by more than 150,000 people  Classifications were as good as those from professional astronomers  Further projects in astronomy, climatology, biology, humanities… www.galaxyzoo.org
  22. 22. Research data typology  Commissioned by RLUK  Aim: to help librarians improve their ability to engage with researchers on RDM matters; and to enable them to acquire a better understanding of the needs of researchers  A resource structured around a suggested typology of research data, looking at different ways in which data might be categorised
  23. 23. Broad data types 1. How do researchers generate and process data, and for what purpose? 1.1 Method of creation and collection of research data: where the data comes from 1.2 Readiness of research data: extent to which data has been processed 1.3 Use of research data: researchers' main purpose for accessing and using data 2. In what file formats, media and volumes do researchers generate data? 2.1 Medium and format for research data: objects in which data is captured and recorded, electronic storage and file types 2.2 Electronic data volumes: size of files (this is subjective, and based largely on the perception of researchers 3. How do researchers manage and store their data? 3.1 Storage of research data: where and how data is kept 3.2 Types of metadata: not an exhaustive list, but these are widely‐recognised metadata standards 3.3 Metadata standards 3.4 Degree of openness: founded on Royal Society's categorisation of 'intelligent openness' 3.5 Licensing of research data: legal rights appertaining the use of the data
  24. 24. An expandable resource  A scaffold onto which disciplinary examples can be hung  Dynamic resource: community input (from librarians, but maybe others too?), crowdsourcing  Turning it into an online interactive tool  Refreshing, curating, adapting the resource  Basic introduction at http://www.powtoon.com/show/fZDm1s0W6TI/research‐data‐typology‐for‐rluk‐ draft/
  25. 25. Conclusions  Lots of work still to do!  Domains different in all respects: data, methods, key RDM concerns, level of infrastructure and support…  Differences exist at sub‐discipline level  Need to understand the area  Developing and using RLUK’s typology
  26. 26. How to plug the gaps?  Dozens of different repositories or databases specialising in sub‐domains or data types, but still major gaps – Shared services? – Institutional services – specialising rather than generic? – Role of publishers and learned societies? – Funder calls for domain specific infrastructure? – Unis to support ground‐up development of tools / services? • How can the sector help domain‐specific solutions to mature and thrive?

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