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RELU Conference, 20 January 2005 RELU Data Support Service RELU-DSS Data Management Workshop Louise Corti and Isabella Tindall.

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Presentation on theme: "RELU Conference, 20 January 2005 RELU Data Support Service RELU-DSS Data Management Workshop Louise Corti and Isabella Tindall."— Presentation transcript:

1 RELU Conference, 20 January 2005 RELU Data Support Service RELU-DSS Data Management Workshop Louise Corti and Isabella Tindall

2 RELU Conference, 20 January 2005 Workshop overview Guidance for creating and sharing high quality data Will cover the key practical, technical, legal and ethical issues including: An overview of the RELU themes and projects Data Management Policy and the RELU Data Support Service ESRCs and NERCs existing Datasets Policies Accessing ESRC and NERC archived data holdings Data held by third parties QA and data management plans Data formats, metadata and standards that allow for longer term sharing and archiving Ethical and legal issues Questions/Discussion

3 RELU Conference, 20 January 2005 RELU Programme Rural Economy and Land Use Programme Harnessing the sciences for sustainable rural development: Rural areas in the UK are experiencing a period of considerable change. The rural economy and land use programme aims to advance understanding of the challenges caused by this change today and in the future. Interdisciplinary research is being funded between 2004 and 2009 in order to inform policy and practice with choices on how to manage the countryside and rural economies. The rural economy and land use programme enables researchers to work together to investigate the social, economic, environmental and technological challenges faced by rural areas. The programme will encourage social and economic vitality of rural areas and promote the protection and conservation of the rural environment.

4 RELU Conference, 20 January 2005 Themes and data RELU themes: –A The Integration of Land and Water Use –B The Environmental Basis of Rural Development –C Sustainable Food Chains (Call 1) –D Economic and Social Interactions with the Rural Environment Call 1: 27 projects funded; smaller pilots/ scoping/capacity building and 8 major data research projects Programme is both using and creating a variety of data sources Disparate types of data – social and environmental and biological data

5 RELU Conference, 20 January 2005 Call 1: Research projects Eating Biodiversity: An Investigation of the Links between Quality Food Production and Biodiversity Protection Comparative Assessment of Environmental, Community & Nutritional Impacts of Consuming Fruit & Vegetables Produced Locally and Overseas Biological Alternatives to Chemical Pesticide Inputs in the Food Chain: An Assessment of Environmental and Regulatory Sustainability Warmwater Fish Production as a Niche Production and Market Diversification Strategy for Organic Arable Farmers with Implications for Sustainability and Public Health Implications of a Nutrition Driven Food Policy for Land Use and the Rural Environment Sustainable and Holistic Food Chains for Recycling Livestock Waste to Land Integration of Social and Natural Sciences to Develop Improved Tools for Assessing and Managing Food Chain Risks Affecting the Rural Economy Re-Bugging the System: Promoting Adoption of Alternative Pest Management Strategies in Field Crop Systems

6 RELU Conference, 20 January 2005 RELU Data Management Policy The data management policy enhances the capabilities for interdisciplinarity and therefore improves the ability of the research community to: apply learning from one field to another combine different methodological approaches and sources of information cross-fertilise ideas and concepts understand scientific, technological and environmental problems in their social and economic contexts

7 RELU Conference, 20 January 2005 Policy principles Publicly funded research data are a valuable, long term resource To ensure maximum research exploitation data must be managed effectively from day-1 Researchers must collect data in such a way as to ensure longer term sharing and manage their data effectively during the life of a project RELU funds will support data management through the life of the project Data must be made available by researchers for archiving: ESRC and NERC supported data centres provide long- term, post-project data management

8 RELU Conference, 20 January 2005 RELU Data Support Service Set up to provide a support service for RELU researchers and staff to gain information and guidance on issues surrounding longer-term data sharing and preservation Joint support service run by: –ESRC/JISC supported UK Data Archive at Essex –The NERC-supported Centre for Ecology & Hydrology Funded for one year supporting one FTE and outreach activities: 1 Jan 05 – 31 Dec 05

9 RELU Conference, 20 January 2005 RELU-DSS a data management advisory and support service for Call 1 award holders and Call 2 applicants and successful award holders a web-based information portal that will provide –expert guidance on data management issues –a searchable meta-data catalogue, detailing the data that RELU award-holders are intending to produce a programme of outreach and training aimed at RELU award holders the facilitation of access to key external data sources for RELU projects, where required guidance to the PMG and data sub-group on data management issues and longer-term costing for supporting RELU projects data management

10 RELU Conference, 20 January 2005 Research Council Data Policies RELU Data Management Policy builds on : NERC data policy found in the Data Policy Handbook available from the NERC web site book.pdf ESRC Datasets Policy found in the ng/sec17.asp

11 RELU Conference, 20 January 2005 ESRC Datasets Policy – what is expected of award holders? to preserve and share data from ESRC funded research funding allowed to prepare data for archiving all award-holders must offer data for deposit to the ESDS within 3 months of the end of the award any potential problems should be notified to the ESDS at the earliest opportunity final payment will be withheld if dataset has not been deposited within 3 months of the end of the award, except where a waiver has been agreed in advance

12 RELU Conference, 20 January 2005 NERC Data Policy – Thematic Programmes all managers of NERC programmes are expected to be familiar with the Policy scientists are expected to consider all the scientific data management implications of their projects at the planning stage (and before submitting grant applications), consulting the Designated Data Centres (DDCs) responsible for scientific data in their subject area. The appropriate DDC should be consulted as soon as it is clear what datasets will be emerging from the project. At the end of their projects grant holders are required to offer to deposit with NERC a copy of datasets resulting from their research

13 RELU Conference, 20 January 2005 Longer-term data sharing data centres /archives make (selected) data created available to other bona fide researchers safeguards to protect the interests of the original collector, who may retain Intellectual Property Rights preserve data using up-to-date curation systems and keep apace with technology and data trends

14 RELU Conference, 20 January 2005 RELU Theme C data types Social data – people based –Micro (survey) Household or individual level attributes Behaviour, attitudes and options Business/company –Farm level data –Aggregated UK Census e.g. small area statistics) Retail statistics health indicators GIS/Spatial data geographically referenced environmental databases –Ordnance survey –Road networks –Settlement

15 RELU Conference, 20 January 2005 RELU Theme C data types continued Water quality, land fill, air quality, emission levels Soil data, eg mineral composition Ecological data, animal and bird distributions Agricultural census Climate and meteorological data River flow data Biochemical data relating to foods/habitats

16 RELU Conference, 20 January 2005 Existing 3 rd party datasets Research Council data centres –Rothmansted (BBSRC experimental samples of crops and soils) –Economic and social data service (eg ESRC Health and Lifestyle survey) –EDINA/UK Borders (boundary data for admin areas) Public/Private Research institutes –Macaulay soils and derived; climate; land cover; land capability data De partment for Environment, Food and Rural Affairs (DEFRA) eg Farm Business survey Scottish Executive Environment and Rural Affairs Department (SEERAD) Environment Agency (EA) National Soil Research Institute Met Office

17 RELU Conference, 20 January 2005 Use of 3 rd party datasets 3 rd parties likely to require RELU to: –Identify one point of contact for discussing data issues E.g. a NERC Data Centre for EA datasets –All partners in a project to sign licenses for use of data –The Data Centre to be responsible for issuing licenses to other projects wishing to use the same data –The Data Centre to distribute the data, once licenses have been signed

18 RELU Conference, 20 January 2005 Access to ESRC/NERC data resources

19 RELU Conference, 20 January 2005 ESRC/JISC Data Centre national data archiving and dissemination service, running from 1 Jan jointly supported by: –Economic and Social Research Council –Joint Information Systems Committee partners: –UK Data Archive (UKDA), Essex –Manchester Information and Associated –Services (MIMAS), Manchester –Cathie Marsh Centre for Census and Survey Research (CCSR), Manchester –Institute of Social and Economic Research (ISER), Essex

20 RELU Conference, 20 January 2005 ESDS overview ESDS Management –central help desk service; coherent and flexible collections development policy; central registration service; universal data portal ESDS Access and Preservation –collections development strategy; ingest activities - including data and documentation processing; metadata creation; data dissemination services; long-term preservation Specialist data services –ESDS Government –ESDS International –ESDS Longitudinal –ESDS Qualidata – dedicated web sites data and documentation enhancements tailored user support outreach and training

21 RELU Conference, 20 January 2005 ESDS Holdings Data for research and teaching purposes and used in all sectors and for many different disciplines official agencies - mainly central government individual academics - research grants market research agencies public records/historical sources links to UK census data qualitative and quantitative international statistical time series access to international data via links with other data archives worldwide history data service in-house (AHDS) 4,000+ datasets in the collection 200+ new datasets are added each year 6,500+ orders for data per year 18,000+ datasets distributed worldwide pa

22 RELU Conference, 20 January 2005 The large-scale government surveys General Household Survey Labour Force Survey Health Survey for England/Wales/Scotland Family Expenditure Survey British Crime Survey Family Resources Survey National Food Survey/Expenditure and Food Survey ONS Omnibus Survey Survey of English Housing British Social Attitudes National Travel Survey Time Use Survey

23 RELU Conference, 20 January 2005 Benefits of the large-scale government datasets good quality data –produced by experienced research organisations –usually nationally representative with large samples –good response rates –very well documented continuous data –allows comparison over time –data is largely cross-sectional hierarchical data –individual and household –intra-household differences –household effects on individuals Percentage of women aged cohabiting General Household Survey

24 RELU Conference, 20 January 2005 Search on Environmental 200+ datasets found

25 RELU Conference, 20 January 2005 Types of qualitative data diverse data types: in-depth interviews; semi-structured interviews; focus groups; oral histories; mixed methods data; open- ended survey questions; case notes/records of meetings; diaries/research diaries multimedia: audio, video, photos and text (most common is interview transcriptions) formats: digital, paper, analogue audio- visual data structures - differ across different document types

26 RELU Conference, 20 January 2005 International data providers International Monetary Fund OECD United Nations World Bank Eurostat International Labour Organisation UK Office for National Statistics freely available to UK HE/FE – data licensing costs are paid by ESRC datasets delivered over the web via Beyond 20/20 Databanks cover: economic performance and development trade, industry and markets employment demography, migration and health governance human development social expenditure education science and technology land use and the environment

27 RELU Conference, 20 January 2005 ESDS: Online access to data and user guides web pages –easy to navigate format –web catalogue with variable level searching –subject browsing and major series –free web access to online doc - pdf user guides and forms registration –one-off registration with userid/password –online account management and Shopping Basket ordering –data are freely available for the majority of users –One-stop Athens authentication data download and online browsing –web download in various software formats - SPSS, STATA, tab-delimited, word –Nesstar – online data analysis and visualisation –ESDS International online system –ESDS Qualidata online browsing system

28 RELU Conference, 20 January 2005 NERC Data Centres NERCs data holdings – core asset Network of 7 Designated Data Centres who are responsible for managing NERC funded data and implementation of the NERC Data Policy data centres Central directory – the NERC metadata gateway E-Science funded NERC Data Grid under development

29 RELU Conference, 20 January 2005 NERC Designated Data Centres Antarctic Environmental Data Centre: Responsible for all NERC's data from the Antarctic, regardless of discipline. British Atmospheric Data Centre: Responsible for atmospheric sciences data. British Oceanographic Data Centre: Responsible for marine data. National Geosciences Information Service: Responsible for geosciences data. National Water Archive: Responsible for NERC's hydrological data and for the Government's National River Flow Archive. Environmental Information Centre: Responsible for all other NERC terrestrial and freshwater data. NERC Earth Observation Data Centre: Responsible for NERCs non-discipline-related remotely sensed data of the surface of the Earth acquired by satellite and airborne sensors.

30 RELU Conference, 20 January 2005 NERC Data Centre Holdings The NERC MetaData Gateway simultaneously searches the catalogues of data held at several of the NERC designated data centres.

31 RELU Conference, 20 January 2005 QA and Data Management Plans

32 RELU Conference, 20 January 2005 Data Management Plan proforma to complete (Section 3 of the Project Communication and Data Management Plan) highlighting data management and custody issues at an early stage providing a basis for quality assurance within the Programme providing a basis from which award holders and the Programme Director can report and monitor project and overall RELU Programme progress

33 RELU Conference, 20 January 2005 Data management Award holders will be required to provide full metadata together with a description of the datasets which their project generates –metadata is the information necessary to interpret, understand and use a given dataset without reference to the original data collector Agree the technical arrangements for data management and archiving (including decisions concerning final archiving destination for project data sets –formats for supply of data –licence agreements; IPR etc.

34 RELU Conference, 20 January 2005 Information required from plan requirements for access to existing datasets details of new and derived datasets to be produced quality assurance of data formats and standards data description and documentation ethical, legal issues and IPR resolution data back-up procedures, security archiving data (for Research Council data archives) data management representative RELU-DSS helps support these areas

35 RELU Conference, 20 January 2005 Quality control and data management issues Survey data Qualitative data Environmental data

36 RELU Conference, 20 January 2005 Characteristics of a good archived research collection Life cycle approach taken accurate data, well organised and labelled files appropriate measurement of key concepts supporting data/documentation should be deposited to a standard that would enable them to be used by a third partycreated –major stages of research recorded –research/measurement instruments documented data that can be stored in user-friendly dissemination formats, but can also be archived in a future-proof preservation format consent, confidentiality & copyright resolved

37 RELU Conference, 20 January 2005 ESDS: Supporting documentation To produce catalogue record and user guide –funding application –questionnaire/Interview schedules –description of methodology (details of sample design, response rate, etc) –codebook(variable names, variable descriptions, code names and variable formatting information) –technical report describing the research project. –communication with informants on confidentiality –Coding schemes / themes –End of award report –software description/versions used –bibliographies, resulting publications –code used to create derived variables or check data (e.g. SPSS, STATA or SAS command files). Anything that adds insight or aids understanding and secondary usage

38 RELU Conference, 20 January 2005 Standardised description (metadata) fields taken from DDI specification for social science datasets

39 RELU Conference, 20 January 2005 Survey data - variables

40 RELU Conference, 20 January 2005 Labelling of survey data all variables should be named. Variable names should not exceed 8 characters where possible, as the most common format for disseminating data is SPSS all variables should be labelled. Labels should be brief (preferably < 80 characters), but precise and always make explicit the unit of measurement for continuous (interval) variables. Where possible, all variable labels should reference the question number (and if necessary questionnaire). For example, the variable q11bhexc might have the label q11b: hours spent taking physical exercise in a typical week. This gives the unit of measurement and a reference to the question number (q11b), so the user can quickly and easily cross-reference to it

41 RELU Conference, 20 January 2005 Labelling of survey data II for categorical variables, all codes (values) should be given a brief label (preferably < 60 characters). For example, p1sex (gender of person 1) might have these value labels: 1 = male, 2 = female, -8 = dont know, -9 = not answered where possible, all such labelling should be created and supplied to the UKDA as part of the data file itself. This is the expectation with data supplied in one of the three major statistical packages - SPSS, STATA or SAS.

42 RELU Conference, 20 January 2005 QA survey data: validation checks Computer aided surveys (CAPI, CATI or CAWI) these are the most accurate way of gathering survey data, but the software (e.g. Blaise) and hardware (e.g. a laptop for every interviewer) may be beyond project resources computer aided surveys allow one to build in as many logical checks - on question routing and responses - as is possible at the point of data creation Non computer aided surveys less control over initial responses, but checks can performed: –at the point of data entry/transcription if data entry software is used. However, there are few cheap data entry packages around –the only feasible option may be to enter data without checks directly into a spreadsheet style interface (e.g. Excel worksheet, SPSS data view), and perform validation checks afterwards - via command files in statistical packages or Visual Basic code in Excel or Access

43 RELU Conference, 20 January 2005 An example of data seemingly untouched by the human eye : Originating error in text variables: OccupationDescription of Occupation sole traderpurveyor of seafood Propagated error in derived numeric variables: Respondent was coded under the standard occupational (SIC) code relating to food retailers: 52.2 Retail sale of food, beverages and tobacco in specialised stores

44 RELU Conference, 20 January 2005 Identifiers Direct' and 'indirect' identifiers may threaten confidentiality Direct identifiers may have been collected as part of the survey administration process and include names, addresses including postcode information, telephone number etc. Indirect identifiers are variables which include information that when linked with other publicly available sources, could result in a breach of confidentiality. This could include geographical information, workplace/organisation, education institution or occupation

45 RELU Conference, 20 January 2005 Quantitative data Remove the identifier from the dataset Aggregate/reduce the precision of a variable –record the year of birth rather than the day, month and year; record postcode sectors (first 3 or 4 digits) rather than full postcode Bracket a coded (categorical) variable –aggregated SOC up to 'minor group' codes by removing the terminal digit Generalise the meaning of a nominal (string) variable Restrict the upper or lower ranges of a continuous variable

46 RELU Conference, 20 January 2005 Online access to data NESSTAR: browse detailed information (metadata) about these data sources, including links to other sources do simple data analysis and visualisation on microdata bookmark analyses download the appropriate subset of data in one of a number of formats (e.g. SPSS, Excel) Data,must be perfect - 100% labelled

47 RELU Conference, 20 January 2005 Derived and aggregated products Permission to share and IPR is main issue Range of potential parties with interest: –Owners, funders, data gatherers, employers other stakeholders, etc. All original source information must be recorded

48 RELU Conference, 20 January 2005 Transcribing qualitative data integrated into the ongoing research – budget accordingly full transcriptions or summaries costs and benefits; –self transcription –internal team transcription –external transcription full transcriptions; –consistent layout –speaker tags –line breaks –header with identifier / other details –checked for errors

49 RELU Conference, 20 January 2005 Qualitative data: identifiers removed Scheme devised – different for each dataset Ideally should reflect any pseudonyms used in publications Confidentiality respected Anonymisation? Problems of anonymisation –Applied too weakly –Applied to strongly –Timing –Potential for distortion –Examples User undertakings Appropriate and sympathetic

50 RELU Conference, 20 January 2005 Qualitative Research e.g set of in-depth interviews Data list: list of contents of research collection acts as a point of entry for secondary user qualitative data: excel template interviewee/case study characteristics

51 RELU Conference, 20 January 2005 Online access to qualitative data new emphasis on providing direct access to collection content –supports more powerful resource discovery –greater scope for searching and browsing content of data (supplementary to higher level study-related metadata) –since users can search and explore content directly… can retrieve data immediately providing access to qualitative data via common interface (EDSD Qualidata Online) supporting tools for searching, retrieval, and analysis across different datasets Means that data must be accurate and standardised

52 RELU Conference, 20 January 2005

53 Back up and security Digital, paper and audio media are fragile. Digital media are even easier to change/copy/delete! a good backup procedure will protect against a range of mishaps such as: –accidental changes to data –accidental deletion of data –loss of data due to media or software faults –virus infections & hackers – catastrophic events (such as fire or flood) Back up frequently, retain off site copies Consider storage conditions, fireproofing etc.

54 RELU Conference, 20 January 2005 ESDS in-house processing in-house data processing –cleaning up research data –Collating documentation received from depositor –repairing minor errors –meeting users expectations –cannot engage in major processing tasks unless destined for publishing into online systems

55 RELU Conference, 20 January 2005 Environmental Data

56 RELU Conference, 20 January 2005 Example: LOCAR Programme To better understand the hydrological, physical, chemical and biological processes operating in lowland catchments To improve modelling to support the integrated management of lowland catchment systems To create a database –£7.75 Million –Three catchments –12 Research projects –Field Programme

57 RELU Conference, 20 January 2005 Flow of data through LOCAR CST User Data Centre

58 RELU Conference, 20 January 2005 Acquire major datasets Provide data to LOCAR Scientists Establish standards for data definition and exchange Receive data and model output from scientists Publish appropriate data at the end of the Programme Ensure long term security and availability of LOCAR data Objectives of the LOCAR Data Centre

59 RELU Conference, 20 January 2005 Pang and Lambourn Catchments Site Pang LambournTern Frome Piddle Recharge743 Borehole5139 Water Quality6614 Flow8611

60 RELU Conference, 20 January 2005 Datasets from NERC River Network DTM Land Cover HOST Daily Mean Flows Rainfall Ground Water Level Keyworth Borehole Archive Records Wellmaster Borehole data Geological maps

61 RELU Conference, 20 January 2005 Raingauges Automatic Raingauges –0.2 mm tipping bucket - hourly Manual Raingauges –Checking Automatic gauge Rainwater collector –Rainwater chemistry samples Water levels –Deep boreholes Flow –EA gauging stations –Ultrasonic doppler flow meter Level and Flow

62 RELU Conference, 20 January 2005 Automatic Weather Station Solar & net radiation Wind speed & direction Air temperature Relative humidity Atmospheric pressure Rainfall Soil temperature & heat flux Carbon Dioxide and Water Vapour Fluxes Hydra (Mk 3)

63 RELU Conference, 20 January 2005 Water Quality Temperature Conductivity Dissolved oxygen pH Turbidity River level Automatic water sampler Salmon counts Smolt counts Redd counts Fish surveys River Habitat Surveys Plant surveys (Mean Trophic Rank) Diatom surveys Chironomid Exuviae Macro invertebrate surveys Ecology

64 RELU Conference, 20 January 2005 Soil Moisture Neutron Probe –Soil water content –Radioactive source –Manual Profile Probe –6 shallow depths –Dielectric constant –Automatic Tensiometers –Puncture Tensiometers (Shallow, Manual) –Purgeable Tensiometers (Shallow, Automatic) –Equitensiometers (Deeper, Automatic) –Deep jacking tensiometers (depths up to 60m) Soil Water Chemistry –Suction Samplers Soil Water Potential

65 RELU Conference, 20 January 2005 Set up Tasks Hardware and Software requirements Create dictionaries Load site and instrument data Format conversion facilities Methods QC Meet with 3 rd party suppliers Load 3 rd party & NERC data Liaise with CSTs and PIs Website

66 RELU Conference, 20 January 2005 Operational Tasks: Receive and load: –Field data –Data from researchers Maintenance Data dissemination Develop software Meetings with: –researchers –CSTs –data managers Attend workshops, seminars and annual science meeting Report to steering committee

67 RELU Conference, 20 January 2005 What can the Data Centre offer Scientists? Data –Access to the field programme data –Access to NERC data –Access to third party data Data Management –Data Centre – acquire, store, disseminate, long term storage, standards –Web site

68 RELU Conference, 20 January 2005 What does the Data Centre ask of Scientists? Appoint –Quality and Data Managers Write and Maintain –Quality and Data Management Plans Supply –Data sets and metadata Observe the Data Policy Meet with the Data Centre

69 RELU Conference, 20 January 2005 Access to datasets Build a metadata database Build a thesaurus of terms Provide a web based search tool Later provide web access to the datasets

70 RELU Conference, 20 January 2005 Searching for metadata on the web Search: –by keyword –by project –detailed search –by theme Description of selected dataset: –Title –Abstract –Contact –Extent

71 RELU Conference, 20 January 2005 Ethical and legal issues

72 RELU Conference, 20 January 2005 Up front issues of consent and confidentiality allowing archiving should be included in the project management plan & addressed before data collection starts longer-term rights management in place and IPR issues considered unless a waiver on deposition has been agreed, researchers should not make commitments to informants which preclude archiving their data

73 RELU Conference, 20 January 2005 Consent for archiving anonymity and privacy of research participants should be respected explicit informed consent gained information for research participants should be clear and coherent and include: –purpose of research –what is involved in participation –benefits and risks –storage and access to data –usage of data (current and future uses) –withdrawal of consent at any time –Data Protection & Copyright Acts N.B. Additional measures are needed when participants are unable to consent through incapacity or age reflect needs and views of all works in practice

74 RELU Conference, 20 January 2005 Legal issues in data preparation Duty of confidentiality Law of Defamation Data Protection Act 1998 and EU Directive Copyright Act 1988 Freedom of Information

75 RELU Conference, 20 January 2005 Duty of Confidentiality disclosure of information may constitute a breach of confidentiality and possibly a breach of contract not governed by an Act of Parliament not necessarily in writing can be a legal contractual exemptions are: –relevant police investigations or proceedings –disclosure by court order –public interest - defined by the courts –ethical obligations in cases of disclosure of child abuse

76 RELU Conference, 20 January 2005 Law of Defamation a defamatory statement is one which may injure the reputation of another person, company or business

77 RELU Conference, 20 January 2005 Data Protection Act 1998 eight principles: –Fairly and lawfully processed –Processed for limited purposes –Adequate, relevant and not excessive –Accurate –Not kept longer than necessary –Processed in accordance with the data subject's rights –Secure –Not transferred to countries without adequate protection allows for secondary use of data for research purposes under certain conditions

78 RELU Conference, 20 January 2005 Options for preserving confidentiality anonymisation consent to archive at the time of field work researcher contacts informants retrospectively user undertakings in exceptional circumstances - permission to use or closure of material

79 RELU Conference, 20 January 2005 Copyright Act 1988 developed for the broadcasting industry not research! protection of authors rights multiple copyrights apply: –automatically assigned to the speaker –researcher holds the copyright in the sound recording of an interview obtain written assignment of copyright from interviewee, or oral agreement (license) to use –employer holds the copyright in research data obtain copyright clearance from employer) copyright lasts for 70 years after the end of the year in which the author dies copying work is an infringement unless it is for the purposes of research, private study, criticism or review or reporting current events, and if the use can be regarded as being in the context of 'fair dealing seek legal advice on problem issues

80 RELU Conference, 20 January 2005 Freedom of Information Freedom of Information Act 2000 A statutory right for individuals and organisations to request information held by public authorities. FOI specifically excludes environmental information which is covered by … Environmental Information Regulations 2004 Enables individuals and organisations to obtain environmental information held by public authorities…. Many RELU data sets will fall under the EIRs

81 RELU Conference, 20 January 2005 What is the legislation? Statutory rights of access to information Apply to public authorities – BBSRC, ESRC, NERC and the universities are public authorities Any one, anywhere can request copy of any information you hold – includes data sets Not all information has to be released Must respond to most requests in 20 days

82 RELU Conference, 20 January 2005 Exemptions –information protected by law Dont Panic - not all information has to be made available under FoI & EIRs FOI & EIRs provide a number of exemptions that can be applied to the release of information The presumption is that information will be made available unless for good reason (a public interest test). Exemptions protect scientific output, commercial business and personal information (through the Data Protection Act) Exemptions can be complex and difficult to apply. If in doubt, ask….

83 RELU Conference, 20 January 2005 RELU-DSS The DSS will provide support to RELU award holders (Call 1 and 2) and round 2 applicants for Call 2, through a telephone and help desk, a web portal and a series of training events. dss.asp Tel: or

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