Wir schaffen Wissen heute für morgen. Workshop Research Integrity at PSI 2013 Data management Tuesday June , Louis Tiefenauer, PSI
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1 Wir schaffen Wissen heute für morgen Workshop Research Integrity at PSI 2013 Data management Tuesday June , Louis Tiefenauer, PSI PSI, 10. Juni 2013
2 Program Dur. End Welcome by Thierry Straessle 5 min Ethical issues in data management 40 min Group discussions 40 min Coffee break, informal discussions 20 min Presentation of outcomes from min General discussion moderated by L. Tiefenauer 20 min End Workshop Research Integrity at PSI, Data Management 2013 Seite 2
3 Data management: the last fifty years History: Data are primary resources not only in science Scientific data: Data produced in Science Metadata (connected to) personal data other data You Science as an open enterprise, Open data for open science The Royal Society, June 2012 p. 25 Workshop Research Integrity at PSI, Data Management 2013 Seite 3
4 Motivation Data management introduction The practice of science: Open inquiry is at the heart of the scientific enterprise. Publication of scientific theories - and of the experimental and observational data on which they are based - permits others to identify errors, to support, reject or refine theories and to reuse data for further understanding and knowledge. Science s powerful capacity for selfcorrection comes from this openness to scrutiny and challenge. Science as an open enterprise, Open data for open science The Royal Society, June 2012 Workshop Research Integrity at PSI, Data Management 2013 Seite 4
5 Data management introduction Research Integrity at PSI, EMPA, eawag, WSL Guidelines for Good Scientific Practice ON FRONT PAGE Honesty, openness, self-criticism and fairness are the basis for credibility and acceptance in science. Researchers at PSI are committed to these values and to the guidelines which derive from them. Wahrhaftigkeit, Offenheit, Selbstkritik und Fairness sind die Grundlage für die Glaubwürdigkeit und Akzeptanz der Wissenschaft. Wir Forschende am PSI sind diesen Werten verpflichtet und halten uns an die daraus abgeleiteten Richtlinien. Workshop Research Integrity at PSI, Data Management 2013 Seite 5
6 Science data management Scientific experiments: e.g. Gravitation Experience Observation Hypothesis Experiment design Measurements Idea Proposal Project Appoval Facilities Science Knowledge Results Applications Publication h = g/2 t 2 Data analysis Raw data Data storage Derived Data Workshop Research Integrity at PSI, Data Management 2013 Data curation Seite 6
7 Science data management Raw data Derived data Information Knowledge Applications Metadata simulation theory Observation signal generation numbers properties understanding speed matters e.g. in epidemies analysis description communication coordination publication Data should be: accessible zugänglich intelligible verständlich comprehensible nachvollziehbar usable brauchbar Data storage / property Data reduction Data interpretation Data format Workshop Research Integrity at PSI, Data Management 2013 Seite 7
8 Science data management Data life cycle and research integrity Raw data Storage Duration Access Ownership Metadata Indexing Communication Indenfication sources fabrication, falisification,theft safety and security privacy, fairness, usability freedom of research confidentiality responsibilities (PI) and others Curation Readable data Migration Data (sets) access TechTransfer Derived Data Analysis Group discussion Communication plan Simulations, modelling Interpretation Results Authorship Visualization Conclusions Applications intelligibile, usable data benefit and verifiability Publication fairness (plagiarism) maximise benefit avoid misinterpretation benefit (science, economies, poverty) conflict of interest Workshop Research Integrity at PSI, Data Management 2013 Seite 8
9 Science data management Data management I Acquisition Detectors: Validation Deletion Processing Data storage explosion, at CERN CMS Maintenance Storage Curation Migration Safety (lost) Security (misuse) Deposition of raw data before publication e.g. Bioscience-papers: DNA Proteins Microarrays (-omics) Policy depends on the research field! Scientific practice: Verification of results Workshop Research Integrity at PSI, Data Management 2013 Seite 9
10 Science data management Data management II Retracted papers Restrictions Health Safety (DNA sequence infection) National security (terrorism) Ethical issues (dual use: avian flu paper) Safety and security Honest error, plagiarism 1:1 Personal data Clinical studies (side effects) Data banks (cancer, inheritary disease) Anonymization (how) Informed consent (test person s agreem.) Safe haven Privacy (stigmatization, discrimination) Public health TechTransfer Contract research Patent of process, product, apparatus Patent in force: licensing use, data free Public-private partnership Conflict of interest Independency Freedom of research Workshop Research Integrity at PSI, Data Management 2013 Seite 10
11 Data management guidelines Responsibilities Data pyramide, raw data Society You Public 5 data base scientific raw data base 4 institutions raw data base project raw data clusters individuals (raw) data acquisition Pubmed, Wikipedia Protein (PDB), LHC SLS data Project data Data sheets Accord. Science as an open enterprise, Open data for open science The Royal Society, June 2012 Workshop Research Integrity at PSI, Data Management 2013 Seite 11
12 Data management guidelines Guidelines for good scientific practice (p.26 & 27) General aim: Foster credibility and acceptance of science, efficiency and quality Specific aims: verifiability (reproduction) (p.27), avoid misconduct, fairness (p. 28) Duty of researchers: make use of your data! publish upon completion of a project Transfer them into technologies to the benefit of society Conditions: freedom of research which is restricted by rules (legal and ethical) Workshop Research Integrity at PSI, Data Management 2013 Seite 12
13 Data management guidelines Addressed points in PSI guidelines (code) Primary responsibility: PI Primary (raw) data (verification); processed (derived) data Storage (long-term), deletion, archiving Data cycle: analysis, publication Transfer for applications: technologies Analysis and interpretation: gray zone, self-crtiticism Communication: publish and share (scientific community, public) Rights and duties: sharing, ownership, access, proprietary Workshop Research Integrity at PSI, Data Management 2013 Seite 13
14 Data management policies Regulations what, when, where, how, who, whith Advices what, when, where, how Training help for researcher Policy Strategy & standards Support hard- & software procedures Guidelines what, why, how, who Plan who, what, when, how Workshop Research Integrity at PSI, Data Management 2013 Seite 14
15 Data management policies Points to be addressed (policy, plan, regulations) Responsibilities Application and use (data banks) quality (accessible, intelligible, usable) formats (for verification) storage (length) ( cost: 1 Gb/5 years: 2 $) safety and security (dual use) curation and migration (costs: up to 10% of project cost) access & ownership (collaborations, proprietary) privacy metadata (researcher and users) sharing and communication rules Training and teaching Supports Software data cycle: generation, analysis, curation, visualization Support for data curation: indexing, tracking, Workshop Research Integrity at PSI, Data Management 2013 Seite 15
16 Data management policies Recommendations from Royal Society (2012)* 1. Scientists: create accessable, intelligible and usable data 2. Institution: data communication as a criterion for career promotion 3. Ranking system: institution output indicators (publications, data) 4. Academies, learned societies: promote open science 5. Funding agencies: require data management plan 6. Scientific journal: repository before publication, etc. 7. Data in public interest: industry and regulators agreements 8. Governments: support open science, also by skilled personnel 9. Governance: release privacy rules 10. Good practices: assure safety and security (openness & secrecy) * Science as an open enterprise, Open data for open science The Royal Society, June 2012 Workshop Research Integrity at PSI, Data Management 2013 Seite 16
17 Data management at PSI Supports, tools, rules at PSI Large-scale facilities: acquistion, storage, access, sharing curation, metadata Departments: data storage, analysis, access, proprietary curation AIT: acquisition, format, storage, safety, migration, costs List to be completed in the group discussions! Workshop Research Integrity at PSI, Data Management 2013 Seite 17
18 Scientific data management topics List of topics (I) 1. Responsible actors: experimentor, PI!, supervisors, leaders 2. Data management plan: education, responsibilities, communication 3. Acquisition: raw data, metadata, statistics, formats, fabrication 4. Treatment: analysis, validation (grey zones), processing (falsification), conversion, statistical evaluation, reduction, presentation (tables, graphics, images) 5. Utilization of results: publications, authorship (plagiarism), tech-tansfer, spin-offs 6. Storage and archiving: IT facilities, costs, migration ethical issues Legal and financial issues Workshop Research Integrity at PSI, Data Management 2013 Seite 18
19 Scientific data management topics List of topics (II) 7. Metadata: associated metadata, data-catalogue (privacy, freedom of research) 8. Ownership: research data, patents, external users (scientific, proprietary), theft, metadata, 9. Disclosure practice: ongoing project, auditing (conflict of interest), reviewing, collaborations (NDA) 10. Access: identified persons, passwords strategy, raw data access 11. Deletion: public data, storage 12. Curation: migration, backups, transformation (history) 13. Data sharing: open access, exchangeable formats ethical issues Legal and financial issues Workshop Research Integrity at PSI, Data Management 2013 Seite 19
20 Workshop Research Integrity at PSI, Data Management 2013 Seite 20
21 Scientific data management at PSI Group discussions be back Complete or adjust list of data management list 2. Discuss recommendations 3. Which point is most important for you? 4. Can you give specific recommendations or hints? Workshop Research Integrity at PSI, Data Management 2013 Seite 21
22 Data management for science, recommendations Summary*: Open data to open science 1. Free exchange of data between researchers 2. Research institutions are primary actors (major influencing factors: reward and promotion system) 3. Additional indicators are needed to assess success 4. Promote open science policy by academies 5. Incentives given by funding agencies * Science as an open enterprise, Open data for open science, The Royal Society, June 2012 Workshop Research Integrity at PSI, Data Management 2013 Seite 22
23 Data management for science, recommendations 6. Improve free access to data (raw & processed) for readers 7. Publication of data (negative & null) of public interest 8. Politics and regulations should foster open science 9. Research data management practice (privacy, metadata, risk minimization) 10. Consider security (avoid lost of data) and safety (avoid damage to people) issues Workshop Research Integrity at PSI, Data Management 2013 Seite 23
24 Research Integrity Workshop: Topics 1 Publication / Authorship Research Misconduct FFP (Plagiarism) Data Management Collaborative Science, decided 2014 Future plans: 5 Mentorship 6 Conflicts of Interest / Commitments 7 Peer Review / Audits Workshop Research Integrity at PSI, Data Management 2013 Seite 24
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