The data explosion. and the need to manage diverse data sources in scientific research. Simon Coles
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1 The data explosion and the need to manage diverse data sources in scientific research Simon Coles Director, UK National Crystallography Service
2 Why manage? Volume Day to day coping How to publish?? Scientific Responsibility Accurate recording of the whole experiment Archival & curation Enabling future science Diversity Heterogeneous data Disciplinary differences Institutional boundaries Accountability Auditing and charging Funder mandates 2
3 What can structures be used for? For example Materials science link to properties, starting point for calculations Insight into / control of crystallisation & crystallisability Starting point for computational chemistry Data management is key, because its all about context and linking Good crystallographic practice requires information about the experiment not covered wholly by CIF Not possible to predict how a crystal structure might be used in the future measure everything you possibly can! 3
4 Increasing complexity Even for pure crystallographic studies volumes of data are getting bigger Informatics approaches being adopted in order to develop fundamental understanding, rules etc Must be able to reliably evaluate quality (provenance) to incorporate results of others of others 4
5 The whole research process Papers, articles, presentations, reports Publication Database Publish Research An Idealised Scientific Research Data Lifecycle Model Research Outputs Scholarly Knowledge Validate, Reuse & Repurpose Data Research Concept and/or Experiment Design Write Proposal (include DMP) Write Usage Reports Peer Review Research Prepare Manuscript Prepare Supplementary Data Interpret & Analyse Results Data Reference Linking IPR, Embargo & Access Control Archive, Preservation & Curation Results Data Derived Data Processed Data Analyse Derived Data Discover & Access Documentation, Metadata & Storage (Reference, Provenance, Context, Calibration etc.) Process Raw Data into Derived Data Raw, Correction & Calibration Data Conduct Experiment Generate, Create, & Collect Raw Data Peer-review Proposal Comments, annotations, ratings etc. Start Project User registration data; Instrument allocation data etc. Acquire Sample Risk assessment data; other sample data Appraisal & Quality Control Programs (generate customised software) KEY Research Activity Research Admin Activity Archive Activity Publication Activity Information Flow 5
6 How many standards?! Publicatio n Database Papers, articles, presentations, reports Publish Researc h CCDC; Peer Review Research PDB; IUCr Prepare XML; Manuscript CIF Prepare Supplementa ry Data Write Usage Reports Interpret & Analyse Results Data Referenc e Linking Research Outputs IPR, Embargo & Access Control Archive, Preservation & Curation Analyse Derived Data Scholarly Knowledge Validate, Reuse & Repurpose Data CML; PDBML; Discover & Access TopCAT; TARDIS; ecrystals; DCC Documentation, Metadata & Storage (Reference, Provenance, Context, Calibration etc.) Results Data Derived Data Processed Data Process Raw Data into Derived Data Appraisal & Quality Control Programs (generate customised software) Research Concept and/or Experiment Design Raw, Correction & Calibration Data Conduct Experiment Generate, Create, & Collect Raw Data Write Proposal (include DMP) P&E; DCC; NSF; ICAT; NCS Peer-review Proposal Start Project Comment s, annotation s, ratings etc. User registration data; Instrument allocation data etc. Risk assessme nt data; other sample data Acquire Sample P&E; CIF; imgcif; ICAT; NeXus KEY Research Activity Research Admin Activity Archive Activity Publication Activity Information Flow 6
7 Data management (eco)systems Publication Repository [Results Data] LIMS [User & Sample Data] ELN [Supporting Experiment Data] Datastores [Underlying Diffraction Data] Desktop/Laptop [Everything!] ipad/iphone [Anything!] How to integrate these?? 7
8 Structured Data. i.e. using LIMS, Datastores & Repositories
9 LIMS: Lifecycle Management Proposal Approval Admin Publication Submission Reporting Analysis Experiment 9
10 LIMS: Lifecycle Management Proposal Approval Admin Publication Submission Reporting Analysis Experiment 10
11 LIMS: Lifecycle Management Proposal Approval Admin Publication Submission Reporting Analysis Experiment 11
12 LIMS: Lifecycle Management Proposal Approval Admin Publication Submission Reporting Analysis Experiment 12
13 LIMS: Lifecycle Management Proposal Approval Admin Publication Submission Reporting Analysis Experiment 13
14 Management Across Boundaries Management across facilities (ICAT Information Model) Conceive Research Propose Proposal Publication Analysis Proposal Approval Schedule Experiment Analyse NCS User Publish Approval NCS Submission Experiment Experiment Archive Analyse Publish Central Facility 14
15 Datastores, ICAT & CSMD The Core Scientific Metadata model forms the information model for ICAT & is designed to describe facilities-based experiments User Database System Single Sign On Data Storage/ Delivery System Proposal System ICAT API Publicatio n System e-science Services RDBMS Web Services API Software Repository Command Line Tools Fortran C++ Glassfish / JBOSS Java 15
16 CSMD as a Starting Point Topic Publication Keyword Authorisatio n Investigation Investigator Dataset Sample Sample Parameter Forms the basis for extensions: Datafile Dataset Parameter Parameter - To derived data - To laboratory based science Related Datafile Datafile Parameter - To secondary analysis data - To preservation information - To publication data 16
17 Getting Mobile in the Lab But every crystallographic experiment has unstructured data 17
18 Getting Mobile in the Lab Package & Sample Tube Bulk Sample & Manipulation Mounted Sample Trial Diffraction Pattern 18
19 Getting Mobile in the Lab The completed record 19
20 Unstructured data. Invariably the context for a crystal structure Or The context a crystal structure has to fit into!
21 Laboratory Notebooks All this is not a new problem 21
22 Electronic Lab Notebooks
23 LabTrove 23
24 Imposing structure Planning & Enactment Ontology Plan (Prospective provenance) Realisation Enactment (Retrospective provenance) 24
25 orechem Plan for ecrystals Machine-readable representation of methodology Describes requirements for software and data products 25
26 Bringing it all together.
27 Structures in context of compound, property, etc data RDF (ChemAxiom) 27
28 Structures in the context of traditional publishing ELN providing Electronic Supplementary Information for conventional publication (Chemistry Central: accepted) 28
29 DOI s for Data DataCite Institutional registration of DOI s Promotes rigorous self publishing of data Soton exemplars ecrystals (Repository/structured data) 29
30 DOI s for Data LabTrove (unstructured data) Locked down HTML export of selected records 30
31 SIMS: Sample Information Management System A standard/format for crystallographic sample and experiment data management and archival Supported by CrystalClear and NCS Portal, providing interaction between facility, instruments and CIF, ImgCIF etc 31
32 elnitemmanifest 3 layer metadata model for description, export & packaging This is the first (information) layer leads into knowledge Published through Dial-a-Molecule athttp://wp.me/p2joq6-xf & submitted to J. ChemInf 32
33 Thanks 33
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