2014

Responsible Conduct

of Research

Module 3 Data transparency -

Fact, fraud and flexibility

Course leader/teachers

Research librarian Mette Brandt Eriksen Head of Institute, Kirsten Ohm Kyvik Librarian Jens Dam Research librarian Thea M. Drachen Head of Library Bertil F. Dorch Extensive “adopting and adapting” from course slides by Mickey Gjerris, Lars Holm, Bertil Dorch, Asger V Larsen and Anders Drachen, with permission.

Course teacher

Thea Drachen - background - disclaimer

Form

Today Lecture, buzzings and plenum discussions Homework (approx 2 hours) Prepare/adjust your data management plan Discuss it with your supervisor

How do you ensure good scientific practice in your work?

Buzz

1960s to explain some of the horrors taking place during WW2 Question: “For how long will someone continue to give shocks to another person if they are told to do so, even if they thought they could be seriously hurt?” i.e. will people do morally wrong things just because an authority figure tells them to?

The Stanley Milligram Experiment

”Shock Generator” with 30 switches Moderate (75-120 V), Strong (135 – 180 V),…

Danger – Severe Shock (375- 420 V), XXX (435 – 450 V)

40 volunteers, payment for showing up, could leave anytime Experimenter (actor posing as distinguished professor)

Another participant (also an actor)

”Participants” drew lots about roles in the ”memory and learning experiment”

Lottery was faked, real participant always ”teacher”

eacher

earner

xperimenter

”Teacher” observes “learner” strapped

to chair and electrodes

Then seated unable to see ”learner”

teach word-pairs

chock at fail 15 V stronger each time

“Teacher” given 45V as test

E in same room as T

If T concerned -> E predefined ”prods” to continue the experiment

progressively more authoritarian

L and T in radio contact (pre-recorded audio)

e.g. 75 V: ”Ugh!!!” 180 V: ”Ugh!!! … Let me out of here!” 285 V: Screaming 345+ V: Silence

Thought: only psychopaths will not stop shocking, maybe 1-3% Result: all 40 gave up to 300 V 25 continued to 450 V i.e. 65% never stopped…

Today not allowed: Deceived about purpose Not made aware of consequences Risk of short-term emotional stress Risk of long-term emotional stress (independent study found no long-term effects)

So, as researchers we cannot do this… but the media can!! Experiment remade by BBC in 2009 12 subjects -> 9 went on to administer 450 V

Why? We humans tend to do as told especially from authority persons Appearance

Why am I telling you this

Content

• Integrity and conduct reg. research data • Guidelines • What not to do

• Data • Data management • Where to go with IP and Patents • Good stuff to know

Referring to rules, regulations and established comme-il-faut’s by: •Laws •Research councils •Funding bodies •National and international coda for science ethics •Danish Standards, of which there is no current National standard for research data but this is being amended

Guidelines

Guidelines Councils, funding bodies

• Research Councils UK (RCUK) has a policy of documentation of research including data (April 2013)

• Welcome Trust has a policy in this area • OECD have a policy in this area – and has had it since 2007 • NSF and NIH have policies in this area • EU – It was a policy in FP7, that you should deliver a DMP and

there will be a policy for Horizon 2020 • European Research Council also has policies in this area. • Nature writes: Data sets must be made freely available to

readers from the date of publication, and must be provided to editors and peer-reviewers at submission, for the purposes of evaluating the manuscript.

Guidelines

Guidelines International

Guidelines

1. Integrity:

Researchers should take responsibility for the trustworthiness of their research.

2. Adherence to Regulations: Researchers should be aware of and adhere to regulations and policies related to research.

3. Research Methods: Researchers should employ appropriate research methods, base conclusions on critical analysis of the evidence and report findings and interpretations fully and objectively.

www.singaporestatement.org/

Guidelines

4. Research Records:

Researchers should keep clear, accurate records of all research in ways that will allow verification and replication of their work by others.

5. Research Findings: Researchers should share data and findings openly and promptly, as soon as they have had an opportunity to establish priority and ownership claims.

13. Research Environments: Research institutions should create and sustain environments that encourage integrity through education, clear policies, and reasonable standards for advancement, while fostering work environments that support research integrity.

www.singaporestatement.org/

Guidelines European

Guidelines

European Research Foundation Code of Conduct http://www.ukrio.org/european-science-foundation-the-european-code-of-conduct-for-research-integrity/

Guidelines Danish

Guidelines

FIVU [Uddannelses og forskningsministeriet – Ministry of Higher Education and Science] has a group working on a proposal for a Danish Code of Conduct for Research Integrity. As part of a broader hearing during April/May a conference was held on 9th of May 2014. Work ongoing.

Ministry of Higher Education and Science http://fivu.dk/forskning-og-innovation/rad-og-udvalg/andre-udvalg-og-fonde/arbejdsgruppe-faelles-retningslinjer-for-forskningsintegritet accessed 5th March 2014

No Danish research foundation has policies on data management, but many do on Open Access, and they feel positive about sharing data as well, for the most part and under certain conditions (Research data project under Danmarks Elektroniske Fag og Forskningsbibliotek (DEFF) – interviews w research funds 2013)

Guidelines

The Steering Committee for Danish National Data Management is currently composing a draft for a national standard for data management

Guidelines

Guidelines Sensitive data

Persondataloven – lovtekst: https://www.retsinformation.dk/Forms/R0710.aspx?id=828 Data tilsynet - http://www.datatilsynet.dk/offentlig/kort-om-persondataloven/

Guidelines SDU

Univeristy of Southern Denmark

has called for a review of how faculties handle research data

in relation to data classification including handling of sensitive and personal information

Guidelines

SDUnet: God videnskabelig praksis / Good Scholarly Practice Juridisk kontor / Legal office http://sdunet.dk/Vaerktoejer/love_regler_aftaler/Forskning/God-videnskabelig-praksis.aspx?contentlang=da § 1 Subsection 3. good scholarly practice demands moreover the observance of good research practice. This means that scholarly activity must be performed with respect for such practice and observance of the universally acknowledged methodology and ethical codes of scholarship of the relevant research field, thereby preserving the personal and professional integrity of the scholar concerned. Commissions or omissions which are inconsistent with good research practice include:

Guidelines

§ 1 Subsection 3 (cont’d) … 2. Deliberate misrepresentation of research results or the dissemination of misleading information about one´s own or others´ part in the research, even though the extent and consequences of the irregularity cannot in themselves be termed grave. 3. Conduct which is not in conformity with the guidelines, issued by official and/or professionally-recognised bodies, governing good scholarly practice in the field (e.g. relating to research protocols, data processing, documentation, declaration of authorship, private financial backing, etc.) ...

Guidelines

Guidelines

University of Southern Denmark

Open Science policy

WHAT NOT

TO DO

Fabrication of data

• Inventing data-sets to support hypothesis • Image-construction Hard to interpret as anything but a conscious attempt to cheat ”I accidentally made up the data?”

Grey areas Methods within statistics to deal with missing data • Imputation (replacing missing data with substituted values) • Extrapolating data on the basis of existing data

Falsification of data

• ”Tidy up” data • Delete data that does not ”fit” – data selection • Image-editing • Choose methodology, equipment etc. that gives incorrect but

desired results

• Modifying (misrepresenting) results to support hypothesis

Buzz break

Dr. José M. is beginning his fifth year as an independent researcher. His work is going well. He has published a number of important articles and secured a large grant for future work. Based on this progress, he expects his pending promotion to proceed without problems. Late one afternoon a graduate student hands José two papers written by a senior colleague in his department. She has circled graphs in each of the papers that are clearly the same but reported as representing two different experiments. After checking the graphs carefully and reviewing the supporting data, José agrees that something is wrong. The senior colleague, who will almost certainly be a member of his promotion review, has either made a careless mistake or falsified information in a publication. What should he do?

Case Study

From Steneck’s ”ORI Introduction to the Responsible Conduct of Research”

Buzz break

Whistleblower scheme

In 1998 Andrew Wakefield and colleagues published a paper in the Lancet claiming that the MMR vaccine causes a series of events that include intestinal inflammation, loss of intestinal barrier function, entrance into the bloodstream of encephalopathic proteins, and consequent development of autism. In support of his hypothesis, Dr. Wakefield described 12 children with neurodevelopmental delay (8 with autism). All of these children had gastrointestinal complaints and developed autism within 1 month of receiving MMR. Text from American Academy of Pediatrics accessed 6 March 2014 http://www2.aap.org/immunization/families/autismwakefield.html

Led to a widespread scare of vaccination – with severe direct and indirect effects In 2010 it was found out that the paper was a result of bad scientific practice – and it was retracted from Lancet Although the authors claim that autism is a consequence of gastrointestinal inflammation, gastrointestinal symptoms were observed after, not before, symptoms of autism in all 8 cases. Children with autism were claimed to have low levels of circulating immunoglobulin A (IgA). However, levels reported were within the normal range for that age group. Text from American Academy of Pediatrics accessed 6 March 2014 http://www2.aap.org/immunization/families/autismwakefield.html

About 90% of children in England received MMR at the time this paper was written. Because MMR is administered at a time when many children are diagnosed with autism, it would be expected that most children with autism would have received an MMR vaccine, and that many would have received the vaccine recently. The observation that some children with autism recently received MMR is, therefore, expected. However, determination of whether MMR causes autism is best made by studying the incidence of autism in both vaccinated and unvaccinated children. This wasn't done. Text from American Academy of Pediatrics accessed 6 March 2014 http://www2.aap.org/immunization/families/autismwakefield.html

damage done notion that MMR vaccine causes autism still rampant

May 8, 2013

How far are you willing to go?

Content

• Integrity and conduct reg. research data • Data

• Sampling • Analyses and statistical tests • Fitness for purpose

• Data management • Where you go with IP and Patents • Good stuff to know

Data

Research data is data that is collected, observed, or created, for purposes of analysis to produce original research results.

Boston University http://www.bu.edu/datamanagement/background/whatisdata/

Oberservations (sensor data, telemetry, survey data, sample data, neuroimages). Data captured in real-time. Usually irreplaceable Experimental (gene sequences, chromatograms). Data from lab equipment. Mostly reproducible but can be expensive Simulations (climate models, economic models). Data generated from test models Compiled (text and data mining, compiled database, 3D models, data gathered from public documents). Reproducible but very expensive

Data

Validity Measure what we want to measure

Ability of a research design to test the hypothesis it was designed to test

Results answer what we intend them to answer

Reliability Results can be replicated by others

Generalizability Results have a wider application than merely

the participants and the circumstances of the test

Data

Sampling

Choice of analyses

Choice of statistical tests

Data

Sampling

Choice of analyses

Choice of statistical tests

Data

Sampling

Choice of analyses

Choice of statistical tests

Data

Where to go for support on choice of Sampling methods Analyses Statistical tests

?

Data

Lovpligtig GCP-støtte til forskerinitieret klinisk lægemiddelforskning http://www.gcp-enhed.dk/kontakt/

Data

CONSORT Consolidated Standards of Reporting Trials http://www.consort-statement.org/ various initiatives to alleviate the problems arising from inadequate reporting of randomized controlled trials

Data Biostatistisk vejledningstjeneste

Http://www.sdu.dk/Om_SDU/Institutter_centre/Ist_sundhedstjenesteforsk /Forskning/Forskningsenheder/Epidemiologi/Vejledning

Data

Database enheden http://databaseenheden.dk/ •Nemmere adgang til anvendelsen af databaser til opsamling af data •Udvikler både administrativt-, klinisk- og kvalitets- og forskningsbaserede databaser på regionalt og landsdækkende niveau

Data Sticking to the Protocol

Moving Targets and Iterative Processes

Validity Measure what we want to measure

Ability of a research design to test the hypothesis it was designed to test

Results show what we intend them to show

Reliability Results can be replicated by others

Generalizability Results have a wider application than merely

the participants and the circumstances of the test

Buzz break How do YOU ensure

in YOUR work?

Content

• Integrity and conduct reg. research data • Data • Data management

• Why data management • Metadata – data about data • Data Management Plans • Data sharing • Data BU & repositories • Sensitive data

• Where to go with IP and Patents • Good stuff to know

Why data management

Why data management

• Meet grant and/or institutional requirements • Documentation ensures integrity of data • Increase your research efficiency through documentation • Preserve your data for own (and/or others) future use • Facilitate new discoveries (e.g. meta studies) • Data sets can be systematically published and quoted, thus

contributing to the global impact of your research

Metadata

• Data about data (More than the name you give your file or the folder it is placed in)

• Different formats/standards usually coupled with the data management service provider

• Sometimes initially cumbersome to include but it is crucial for the (/your) future usability of the data and the inclusion process typically lightens with more uploads

• Metadata makes data useable for others and re-useable for you

• Data Documentation Initiative (DDI) - A metadata specification for the social and behavioral sciences http://www.ddialliance.org/

• Dansk Data Arkiv – guidelines and specific requirements for metadata format http://www.sa.dk/dda/

Data management plan

Typically asked to cover:

Description of the data to be collected / created

Standards / methodologies

for data collection and management

Ethics and Intellectual Property concerns or restrictions

Plans for data sharing and access

Strategy for long-term preservation

http://www.dcc.ac.uk/

Data Management plan

• DCC - Digital Curation Centre – UK based (JISC funded) http://www.dcc.ac.uk/

• MANTRA – University of Edinburgh – UK Based http://datalib.edina.ac.uk/mantra/

• Australian National Data Service (ANDS) http://ands.edu.au/

• Stanford University – US Based http://library.stanford.edu/research/data-management-services/data-management-plans

Data management plan

Data management plan

Data management plan

Data management plan

Data management plan

Data management plan

Data management plan

Buzz

What metadata should your datasets contain for you to be able to use them and share them with a collaborator

in 5 years?

Do you have a data management plan for your project?

What does it contain/describe?

Why data sharing

• The national bibliometric research indicator honors published data sets and publications with associated datasets

• Increase the visibility of your research *

* Clinical trials - Piwowar, Heather A., Roger S. Day, og Douglas B. Fridsma. «Sharing Detailed Research Data Is Associated with Increased Citation Rate». PLoS ONE 2, nr. 3 (2007): e308. doi:10.1371/journal.pone.0000308

* Astrophysics - Dorch, Bertil. «On the Citation Advantage of linking to data» (2012). http://hprints.org/hprints-00714715

Data sharing Cancer clinical trials

Data sharing

“We found that cancer clinical trials which share their microarray data were cited about 70% more frequently than clinical trials which do not.”

“This result held even for lower-profile publications and thus is relevant to authors of all trials.”

Data sharing

Data BU & repositories

• Backing up data on “fællesdrevet”… • SDU IT option of hiring serverspace

http://www.sdu.dk/Om_SDU/Faellesomraadet/IT-service/Services/Virtuelserver.aspx

• Commercial services like DropBox (primarily for BackUp, can be used for sharing)

• Dansk Data Arkiv

Is there a repository for you? Probably. http://www.datacite.org/repolist will show you a list.

Disciplinary or institutional repositories? • Disciplinary: eased discovery, probably eased integration

between datasets • Institutional: local support, branded by institution, durability

Data BU & repositories

http://www.sdu.dk/Om_SDU/Faellesomraadet/IT-service/Services/Datacontainer http://sdunet.dk/Vaerktoejer/Vejledninger/IT/Datacontainer.aspx

Data BU & repositories

http://www.sdu.dk/en/om_sdu/institutter_centre/klinisk_institut/forskning/forskningsenheder/open

Data BU & repositories

http://www.sdu.dk/en/om_sdu/institutter_centre/klinisk_institut/forskning/forskningsenheder/open

OPEN is a research infrastructure • supports clinical research in the Region of Southern Denmark • inviting other research groups to join projects • enrich the cohort by asking other scientific questions than

those initially raised • often achieved simply by adding a few extra tests to the

protocol

Data BU & repositories

• ZENODO linked to OpenAIRE (https://www.openaire.eu/) which is linked to EU funding http://zenodo.org/ For researchers, scientists, EU projects and institutions to share and showcase multidisciplinary research results (data and publications) that are not part of existing institutional or subject-based repositories

• DataBox https://data.kb.dk/dvn/ (not endorsed by KU - currently composing needs and standards for an institutional data repository)

Data BU & repositories

http://www.sdu.dk/Om_SDU/Faellesomraadet/IT-service/Services

IT services

Sensitive data

Persondataloven

http://samfund.dda.dk/ddakatalog//MogD/md94.pdf

Sensitive data Datatilsynet http://www.datatilsynet.dk/ Datatilsynet i Region Syddanmark

Sensitive data

Retningslinjer for anmeldelse af forsker-initieret sundhedsforskning i regionerne til Datatilsynet http://www.regioner.dk/sundhed/kvalitet+og+forskning/forskning/retningslinjer+for+anmeldelse+af+forsker-initieret+sundhedsforskning+i+regionerne+til+datatilsynet

Content

• Integrity and conduct reg. research data • Data • Data management • Where to go with IP and Patents • Good stuff to know

IP and Patents Tech Trans office (in Danish)

http://www.sdu.dk/om_sdu/faellesomraadet/sdu+erhverv/teknologioverfoersel

IP and Patents Tech Trans office (in Danish)

http://www.sdu.dk/om_sdu/faellesomraadet/sdu+erhverv/teknologioverfoersel

(MTA) Material Transfer Agreement no signing untill talk w Tech Trans!

Patent application before publishing

IP and Patents

Juridisk kontor (in Danish)

http://www.sdu.dk/om_sdu/faellesomraadet/ledelsessekretariatet/juridisk+kontor

Buzz break

How is integrity in research monitored? Is self-regulation of integrity in research effective? From Steneck’s ”ORI Introduction to the Responsible Conduct of Research”

Content

• Integrity and conduct reg. research data • Data • Data management • Where to go with IP and Patents • Good stuff to know

Further knowledge and training

Research Integrity and courses •Epigeum Research Integrity online course - UK based, but relates to the EU (for Copenhagen University, SDU is considering) http://researchskills.epigeum.com •NIH ethics course http://researchethics.od.nih.gov/

Further knowledge and training

Visibility of research • Blogging • Coordinating online profile, specifically Google Scholar • Figshare

http://figshare.com/ get citation credit for datasets make your research more discoverable, citable and sharable enables you to publish negative results

• Open Access publishing (if it’s not on Google it does not exist)

Further knowledge and training

ORCID http://orcid.org/ Web of Science articles by ”Hansen” published since 2000, in science and technology, yielded more than 50.000 hits. Problem of the who is who. 0,3 mill. Johanssons (Sweden) 2,5 mill. Smiths (USA) 10 mill. Kims (Korea) 100 mill. Wangs (China) ??? mill. Names changed by marriage, divorce, numerology, new employment (name of organization), cultural differences in name order?

Further knowledge and training

Using pictures

Store on intranet only or use permitted pictures only

Further knowledge and training

Using pictures Wikimedia

Further knowledge and training

Using pictures Google Advanced search

Further knowledge and training

Using pictures Google Advanced search

Further knowledge and training

Using pictures Google Advanced search

Further knowledge and training

Further knowledge and training

Putting your own stuff out there Creative Commons

Further knowledge and training

Linking

Can you link to copyright protected material on the internet without ok from the copyright holder?

To legal material is ok unless you link to stuff put on the internet

without the copyrightholders accept or if copy protection has been violated

burden of evidence…