This should probably be titled “are research datasets comprised of facts and does it matter?”. It certainly does appear to matter whether datasets are comprised of facts, as in some legal jurisdictions facts are not copyrightable. If this is so, then without other protection such as the EU Database Right, or perhaps contract law, then there is no basis for licences (you can’t control someone else’s use unless you have a right to exercise that control). This is part of the argument that led Science Commons to abandon attempts to find variants of the Creative Commons licences for datasets and databases, in favour of its proposals for putting datasets into the public domain.
This is an appealing solution in some research contexts, but worrying for other kinds of research. This is not for reasons of profit, but of ethics. Many medical, social science, anthropological, financial and other datasets contain data that are private, perhaps personally, culturally or corporately; these data were usually gathered with some kind of informed consent on use, and have to be protected. They cannot be placed into the public domain. If they are to be made available at all for re-use, there must be terms and conditions attached, ie some kind of licence.
I’m not attempting to argue the legal angle here. But I am interested in the “factness” of the data, that might inform the legal angle.
One might assume the height of Mount Everest is a fact. But check out the Wikipedia article on the subject to see a range of results. One might assume that the physical properties of chemical substances are facts, but check out Chemspider’s approach of assembling different measurements with their provenance (see http://www.chemspider.com/blog/there-are-no-facts-in-science-only-measurement-embedded-within-assumptions.html which links to Jen-Claude Bradley’s earlier UsefulChem article ). Or think of a geospatial database, some elements must be pretty much “skill and judgment” rather than facts, such as the point where a river debouches into the sea. Finally, one might assume that the names of the winners of horse races are facts, and so they are, but only after a race committee has adjudicated on the photo-finish, or whether interference took place.
In practice, what goes into datasets is rarely what is directly measured; it is almost always highly derived through various computations, adjustments and combinations. Environmental sciences can be quite explicit on this, see for example the British Atmospheric Data Centre’s description of the UARS (Upper Atmosphere Research Satellite) data levels. Here level 0 is the raw output data streaming from telemetry and instrumentation, effectively at the level of voltage changes; it is devoid of context. Level 1 data has been converted to the physical properties being measured, but will still be in formats tied to the instrument. Level 2 is post-calibration, and would refer to entities such as calculated geophysical profiles. Level 3 would be gridded and interpolated, and at this level there might be no clear correspondence with any observations (but there should be a clear computational lineage or provenance path linking these steps).
So we seem to be in a situation where datasets contain highly derived data, at some creative distance from direct observations, and what we think of as facts are (or ought to be) contestable consensus based on potentially conflicting evidence,
In fact (hah!) after a while it becomes hard to think of any good example of real science/research data that are facts. The question is, does this matter enough to make any difference?
Showing posts with label Legal issues. Show all posts
Showing posts with label Legal issues. Show all posts
Thursday, 26 March 2009
Wednesday, 10 December 2008
IDCC4: DCC Blawg "consent" post
Just a little plug here; my colleague Mags McGeever, who provides legal information for the DCC, has her own blog (The DCC Blawg), and has recently been providing a bit more comment and opinion rather than just information (that's not legal opinion, mind!). Her recent post on the variant of "informed consent" used by Generation Scotland, mentioned by David Porteous in his opening Keynote at the Digital Curation Conference, is interesting, and some of her other recent articles are worth a look, too.
Monday, 8 December 2008
Wilbanks on the Control Fallacy: How Radical Sharing out-competes
Closing the first day of the International Digital Curation Conference, and as a prelude to a substantial audience discussion, John Wilbanks from Science Commons outlined his vision and his group’s plans and achievements. His slides are available on Slideshare and from the IDCC web site.
John suggests that the only real alternative to radical sharing is inefficient sharing! Science (research and scholarship, to be more general) is in a way not unlike a giant Wikipedia, an ever-changing consensus machine, based on publishing (disclosing, making things public); advances by individual action, and by discrete edits (ie small changes to the body of research represented by individual research contributions). Unlike Wikipedia the Science consensus machine is slow and expensive, but it does have strong authentication and trust. It represents an “inefficient and expensive ecosystem of processes to peer-produce and review scholarly content”.
However, disruptive processes can’t be planned for, and when they occur, attract opposition from entrenched interests (open access is one such disruptive system). So a scholarly paper may be thought of as “an advertisement for years of scholarship” (I think he gave a reference but I can’t find it). International research forms a highly stable system, good at resisting change on multiple levels, and in many cases this is a Good Thing. This system includes Copyright (Wilbanks suggested IPR was an unpopular term, new to me but perhaps a US perspective); traditionally, in the analogue world, copyright locks up the container, not the facts. New publishing business models lock up even more rights on “rented” information. If we can deposit our own articles (estimated cost 40 minutes per researcher per year), then we can add further services over our own material (individually or collectively), including tracking use and re-use; then we can out-compete the non-sharers.
Science Commons has been working for the past 2 years focusing their efforts in both the particular (one research domain, building the Neurocommons) and the general; their approach requires sharing. They discovered two things quite early on: first that international rules on intellectual property in data vary so widely that building a general data licence to parallel the Creative Commons licences for text was near impossible, and second that viral licences (like the Creative Commons Share-Alike licences, and the GPL) act against sharing, since content under different viral licences cannot be mixed. So their plan is to try putting their data into the public domain as the most free approach. They use a protocol (not licence) for implementing open data. reward comes through trademark, badging as part of the same “tribe”. Enforcement doesn’t apply; community norms rule, as they do in other areas of scholarly publishing. For example attribution is a legal term of art (!!), but in scholarly publishing we prefer the use of citations to acknowledge sources, with the alternative for an author being possible accusations of plagiarism, rather than legal action (in most cases).
There was some more, on the specifics of their projects, on trying to get support in ‘omics areas for a semantic web of linked concepts, built around simple, freely alterable ontologies; how doing this will help Google Scholar rank papers better. Well worth a look at the slides (worth while anyway, since I may well have got some of this wrong!). But Wilbanks ended with a resounding rallying cry: Turn those locks into gears! Don’t wait, start now.
John suggests that the only real alternative to radical sharing is inefficient sharing! Science (research and scholarship, to be more general) is in a way not unlike a giant Wikipedia, an ever-changing consensus machine, based on publishing (disclosing, making things public); advances by individual action, and by discrete edits (ie small changes to the body of research represented by individual research contributions). Unlike Wikipedia the Science consensus machine is slow and expensive, but it does have strong authentication and trust. It represents an “inefficient and expensive ecosystem of processes to peer-produce and review scholarly content”.
However, disruptive processes can’t be planned for, and when they occur, attract opposition from entrenched interests (open access is one such disruptive system). So a scholarly paper may be thought of as “an advertisement for years of scholarship” (I think he gave a reference but I can’t find it). International research forms a highly stable system, good at resisting change on multiple levels, and in many cases this is a Good Thing. This system includes Copyright (Wilbanks suggested IPR was an unpopular term, new to me but perhaps a US perspective); traditionally, in the analogue world, copyright locks up the container, not the facts. New publishing business models lock up even more rights on “rented” information. If we can deposit our own articles (estimated cost 40 minutes per researcher per year), then we can add further services over our own material (individually or collectively), including tracking use and re-use; then we can out-compete the non-sharers.
Science Commons has been working for the past 2 years focusing their efforts in both the particular (one research domain, building the Neurocommons) and the general; their approach requires sharing. They discovered two things quite early on: first that international rules on intellectual property in data vary so widely that building a general data licence to parallel the Creative Commons licences for text was near impossible, and second that viral licences (like the Creative Commons Share-Alike licences, and the GPL) act against sharing, since content under different viral licences cannot be mixed. So their plan is to try putting their data into the public domain as the most free approach. They use a protocol (not licence) for implementing open data. reward comes through trademark, badging as part of the same “tribe”. Enforcement doesn’t apply; community norms rule, as they do in other areas of scholarly publishing. For example attribution is a legal term of art (!!), but in scholarly publishing we prefer the use of citations to acknowledge sources, with the alternative for an author being possible accusations of plagiarism, rather than legal action (in most cases).
There was some more, on the specifics of their projects, on trying to get support in ‘omics areas for a semantic web of linked concepts, built around simple, freely alterable ontologies; how doing this will help Google Scholar rank papers better. Well worth a look at the slides (worth while anyway, since I may well have got some of this wrong!). But Wilbanks ended with a resounding rallying cry: Turn those locks into gears! Don’t wait, start now.
Thursday, 4 December 2008
International Digital Curation Conference Keynote
I'm not sure how much I should be blogging about this conference, given that the DCC ran it, and I chaired quite a few sessions etc. But since I've got the scrappy notes, I may try to turn some of them into blog posts. I've spotted blog postings from Kevin Ashley on da blog, and Cameron Neylon on Science in the Open, so far.
Excellent keynote from David Porteous, articulate and passionate supporter of the major “Generation Scotland” volunteer-family-based study of Scottish Health (notoriously variable, linked to money, diet, and smoking, and in some areas notoriously poor). Some interesting demographic images based on Scottish demography 1911, 1951, 2001 and projected to 2031, show the population getting older as we know. It’s not only the increasing tax burden on a decreasing proportion of workers, but also the rise of chronic disease. The fantastically-named Grim Reaper’s Road Map of Mortality shows the very unequal distribution, with particular black spots in Glasgow. About half of these effects are “nurture” and about half are “nature”, but really it’s about the interplay between them.
He spoke next about genetics, sequencing and screening. Genome sequencing took 13 years and cost $3B, now a few weeks and $500K, next year down to a few $K? Moving to a system where we hope to identify individuals at risk, start health surveillance, understand the genetic effects and target rational drug development, we hope reducing bad reactions to drugs.
Because of population stability, the health and aging characteristics, and a few legal and practical issues (such as cradle-to-grave health records), it turns out that Scotland is particularly suited to this kind of study. Generation Scotland is a volunteer, family-based study (illustration from the Broons!). There are Centres in Edinburgh, Glasgow, Dundee, Aberdeen; no-one should be more than an hour’s travel away etc. Major emphasis on data collection, curation and integration through an integrated laboratory management system, in turn linking to the health service and its records. Major emphasis on security and privacy. Consent is open consent (rather than informed consent), but all have a right to withdraw from the study, and must be able to withdraw any future use of their data (only 2 out of nearly 14,000 have withdrawn so far!).
This wasn’t a talk about the details of curation, but it was an inspiring example of why we care about our data, and how, when the benefits are great enough and the planning and careful preparation are good enough, even major legal obstacles can be overcome.
Excellent keynote from David Porteous, articulate and passionate supporter of the major “Generation Scotland” volunteer-family-based study of Scottish Health (notoriously variable, linked to money, diet, and smoking, and in some areas notoriously poor). Some interesting demographic images based on Scottish demography 1911, 1951, 2001 and projected to 2031, show the population getting older as we know. It’s not only the increasing tax burden on a decreasing proportion of workers, but also the rise of chronic disease. The fantastically-named Grim Reaper’s Road Map of Mortality shows the very unequal distribution, with particular black spots in Glasgow. About half of these effects are “nurture” and about half are “nature”, but really it’s about the interplay between them.
He spoke next about genetics, sequencing and screening. Genome sequencing took 13 years and cost $3B, now a few weeks and $500K, next year down to a few $K? Moving to a system where we hope to identify individuals at risk, start health surveillance, understand the genetic effects and target rational drug development, we hope reducing bad reactions to drugs.
Because of population stability, the health and aging characteristics, and a few legal and practical issues (such as cradle-to-grave health records), it turns out that Scotland is particularly suited to this kind of study. Generation Scotland is a volunteer, family-based study (illustration from the Broons!). There are Centres in Edinburgh, Glasgow, Dundee, Aberdeen; no-one should be more than an hour’s travel away etc. Major emphasis on data collection, curation and integration through an integrated laboratory management system, in turn linking to the health service and its records. Major emphasis on security and privacy. Consent is open consent (rather than informed consent), but all have a right to withdraw from the study, and must be able to withdraw any future use of their data (only 2 out of nearly 14,000 have withdrawn so far!).
This wasn’t a talk about the details of curation, but it was an inspiring example of why we care about our data, and how, when the benefits are great enough and the planning and careful preparation are good enough, even major legal obstacles can be overcome.
Monday, 17 November 2008
Project data life course
This blog post is an attempt to explore the “life course” of an arbitrary small to medium research project with respect to data resources involved in the project. (I want to avoid the term life cycle, since we use this in relation to the actual data.)
This seems like a useful exercise to attempt, even if it has to be an over-generalisation, as understanding these interactions may help to define services that support projects in managing and curating their data better, and a number of such services are suggested here. This is about “Small Science”; James M. Caruthers, a professor of chemical engineering at Purdue University has claimed ‘Small Science will produce 2-3 times more data than Big Science, but is much more at risk’ (Carlson, 2006). Large and very large projects tend to have very particular approaches, and probably represent significant expertise; I’ll not presume to cover them.
Is this generalisation way off the mark? I’d really like to know how to improve it, accepting that it is a generalisation!
The overall life course of a project (I suggest) is roughly:
Pre-proposal stage
At the pre-proposal stage, you and your potential partners will mostly be concerned with working out if your research idea is likely to be feasible, how and with roughly what resources. During these conversations, you will need to identify any external or already existing data resources that will be required to enable the project to succeed. You should also start to identify the data that the project would create, or perhaps update, and how these data will support the research conclusions. In doing so, attention must be paid to resource constraints, not necessarily financial, but also legal and ethical issues affecting use of the data. However, discussions are more likely to be generic rather than focused! Thus begins the impact on curation…
Curation service: 5-minute curation introduction, with pointers to more information…
Curation service: Help Desk service. Ask a Curator?
Proposal stage
Apart from the obvious focus on the detailed plan for the research itself, this stage is particularly constrained by funder requirements. Increasingly, these requirements include a data management plan. This is unlikely to be a problem for large projects, which will already have spent some time (often lots of time) considering data management issues, but may well be a new requirement for smaller projects. However, forcing attention to be paid to data management is definitely a Good Thing! I’ll have a closer look at data management plans in a later blog post.
Curation service: Data Management Plan templates
Curation service: Data Management Plan online wizards?
Curation service: consultancy to help you write your Data Management Plan?
Key issues that the project should address, and which should be reflected in the data management plan, include the standards and encodings for analysing, managing and storing data. While these must be determined by a combination of local requirements, such as the availability of licences, skills and experience, and the local IT environment, they must take account of community norms and standards. While in some cases maverick approaches can lead to breakthrough science, they can also lead to silo data, inaccessible and not re-usable (sometimes not re-usable even by their creators, after a short while). This stage begins to have a significant effect on curation, as the decisions that are fore-shadowed here will have major effects later on. Repeat after me: curation begins before creation!
Curation service: Guidance on managing data for re-use in the medium to long term
Curation service: registries and repositories to support re-use, sharing and preservation
Curation service: pointers to relevant standards, vocabularies etc.
[Note, we have concerns about the scalability of the latter, which is why the DCC DIFFUSE service is being re-modelled to be more participative.]
Establishment stage
It’s during the establishment phase that the real work is in sight. In the case of a well-established laboratory or research centre, this may merely be “yet another project” to be fitted into well-known procedures, perhaps with some adaptations. But from my observation and anecdotally, there is often a high degree of individual idiosyncratic decision-making taking place here; the focus is often on “getting down to the research”. The consequences of this for curation may be very significant, but are unlikely to be apparent until much later.
The key curation aim is to set the data management plan into operation. This requires ensuring access to external data required by the project (an activity that should have been started, at least in principle, ahead of project approval). While you’re thinking of access, rights and ownership, negotiating and documenting data ownership and access agreements for the data created in the project would be a smart idea (too often ignored, or left until problems begin to arise). Of course, it’s not just rights that are at issue, but also responsibilities, particularly where data have privacy and ethical implications.
Curation service: Briefing papers on legal, ethical and licence issues.
Meanwhile a critical step is establishing the IT infrastructure. While the focus is likely to be on acquiring and testing the data acquisition and analysis equipment, software, capabilities and workflows, in practice the apparently background activities of ensuring adequate data storage, sensible disaster recovery procedures (or at the very least, proper data backup and recovery), and thoughtful data curation processes will be critical for later re-use.
Curation service: Briefing paper on Keeping your data safe?
Curation service: tools for various tasks, eg data audit, risk assessment
It’s worth remembering that there are different kinds of data storage. While you will often be most concerned with temporary or project-lifetime storage, you may need to think about storage with greater persistence. There may be a laboratory or institutional or subject repository, external database or data service that will be critical to your project; negotiating with the curation service providers associated with these services should be an important early step. In effect, right at the start you are planning for your exit! But more on this another time…
Curation service: Guidance on data repositories for projects?
Curation service: thought-stimulating discussion papers, blog posts and case studies, journal, conference, discussion fora…
Project execution
Through the lifetime of your project, your focus will rightly be on the research itself. But you should also take time out to test your disaster recovery procedures, and check on your quality assurance. You care about the quality of your materials and syntheses (or your algorithms, or your documentary sources), you need to care as much about the quality of your data, both as-observed and as-processed. You’ll be collecting a lot of data; if some support your hypothesis and some contradict it, you’ll need to ensure you are collecting (perhaps in laboratory notebooks or equivalent) sufficient provenance and context to justify the selection of the former rather than the latter.
Not sure if there is a curation service here, or if these are good practice research guides for your particular domain… but I guess the same could be said for standards and vocabularies. Question is whether domains HAVE good practice guides for data quality?
As the project goes forward, issues may arise in your original data management plan, and you should take the time to refine it (and document the changes), and your associated curation processes. If you did take idiosyncratic decisions in haste at project establishment, at some point you may need to review them. It’s a warning sign when a staffing change is followed by proposals for major architectural changes!
Finally, at some stage you should test any external deposit procedures built into your plan.
Curation service: Guidance on data deposit…
I don’t think I’m claiming that this is the limit of curation issues during project execution; rather that here the course of projects is so varied that generalisations are even less justifiable than elsewhere. Nevertheless, the analysis here should be strengthened, so input would be helpful!
Project termination stage
As the project comes towards an end, you enter another risk phase. Firstly, most management focus is likely to be on the next project (or the one after that!). Secondly, if employment is fixed term, tied to the particular project, then research staff will have been looking for other posts as the end of the project draws near, and the project may start to look denuded (and the data management and informatics folk may be the most marketable, leaving you exposed). And of course, the final reports etc cannot be written until the project is finished, by which time the project’s staffing resources will have disappeared. It’s the PI’s job to ensure these final project tasks are properly completed, but it’s not surprising that there are problems sometimes!
Curation service: Guidance on (or assistance with) evaluation
During this final phase, you must identify any data resources of continuing value (this should have occurred earlier, but does need re-visiting), and carry out the plans to deposit them in an appropriate location, together with the necessary contextual and provenance information. Easy, hey? Perhaps not: but failure here could have a negative impact on future grants, if research funders do their compliance checking properly.
Curation service: Guidance on appraisal, Guidance on data deposit.
Finally, given that data management plans are currently in their infancy, it’s worth commenting on the plan outcomes in your project final report!
Writing papers
This is not really a project phase as such, as often several papers will be produced at different stages of the project. The key issues here are:
Curation service: Proposals for data citation
Curation service: Suggestions for the Well-supported article
So: the message is that curation should be a constant theme, albeit as background during most of the project execution. But the decisions taken during pre-proposal, proposal and establishment phases will have a big effect on your ability to curate your data, which may affect your research results, and will certainly affect the quality and re-usability of the data you deposit.
Carlson, S. (2006, June 23). Lost in a Sea of Science Data: Librarians are called in to archive huge amounts of information, but cultural and financial barriers stand in the way. The Chronicle of Higher Education, 52, 35.
This seems like a useful exercise to attempt, even if it has to be an over-generalisation, as understanding these interactions may help to define services that support projects in managing and curating their data better, and a number of such services are suggested here. This is about “Small Science”; James M. Caruthers, a professor of chemical engineering at Purdue University has claimed ‘Small Science will produce 2-3 times more data than Big Science, but is much more at risk’ (Carlson, 2006). Large and very large projects tend to have very particular approaches, and probably represent significant expertise; I’ll not presume to cover them.
Is this generalisation way off the mark? I’d really like to know how to improve it, accepting that it is a generalisation!
The overall life course of a project (I suggest) is roughly:
- pre-proposal stage (when ideas are discussed amongst colleagues, before a general outline of a proposal is settled on), followed by
- the proposal stage (constrained by funder’s requirements, and by the need to create a workable project balancing expected science outcomes, resources available, and papers and other reputational advantages to be secured). If the project is funded, there follows
- an establishment phase, when the necessary resources are acquired, configured and tested, (which overlaps and merges with)
- an execution phase when the research is actually done, and
- a termination phase as the project draws to a close.
- Somewhere during this process, one or more papers will be produced based on the research understandings gained, linked to the data collected and analysed.
Pre-proposal stage
At the pre-proposal stage, you and your potential partners will mostly be concerned with working out if your research idea is likely to be feasible, how and with roughly what resources. During these conversations, you will need to identify any external or already existing data resources that will be required to enable the project to succeed. You should also start to identify the data that the project would create, or perhaps update, and how these data will support the research conclusions. In doing so, attention must be paid to resource constraints, not necessarily financial, but also legal and ethical issues affecting use of the data. However, discussions are more likely to be generic rather than focused! Thus begins the impact on curation…
Curation service: 5-minute curation introduction, with pointers to more information…
Curation service: Help Desk service. Ask a Curator?
Proposal stage
Apart from the obvious focus on the detailed plan for the research itself, this stage is particularly constrained by funder requirements. Increasingly, these requirements include a data management plan. This is unlikely to be a problem for large projects, which will already have spent some time (often lots of time) considering data management issues, but may well be a new requirement for smaller projects. However, forcing attention to be paid to data management is definitely a Good Thing! I’ll have a closer look at data management plans in a later blog post.
Curation service: Data Management Plan templates
Curation service: Data Management Plan online wizards?
Curation service: consultancy to help you write your Data Management Plan?
Key issues that the project should address, and which should be reflected in the data management plan, include the standards and encodings for analysing, managing and storing data. While these must be determined by a combination of local requirements, such as the availability of licences, skills and experience, and the local IT environment, they must take account of community norms and standards. While in some cases maverick approaches can lead to breakthrough science, they can also lead to silo data, inaccessible and not re-usable (sometimes not re-usable even by their creators, after a short while). This stage begins to have a significant effect on curation, as the decisions that are fore-shadowed here will have major effects later on. Repeat after me: curation begins before creation!
Curation service: Guidance on managing data for re-use in the medium to long term
Curation service: registries and repositories to support re-use, sharing and preservation
Curation service: pointers to relevant standards, vocabularies etc.
[Note, we have concerns about the scalability of the latter, which is why the DCC DIFFUSE service is being re-modelled to be more participative.]
Establishment stage
It’s during the establishment phase that the real work is in sight. In the case of a well-established laboratory or research centre, this may merely be “yet another project” to be fitted into well-known procedures, perhaps with some adaptations. But from my observation and anecdotally, there is often a high degree of individual idiosyncratic decision-making taking place here; the focus is often on “getting down to the research”. The consequences of this for curation may be very significant, but are unlikely to be apparent until much later.
The key curation aim is to set the data management plan into operation. This requires ensuring access to external data required by the project (an activity that should have been started, at least in principle, ahead of project approval). While you’re thinking of access, rights and ownership, negotiating and documenting data ownership and access agreements for the data created in the project would be a smart idea (too often ignored, or left until problems begin to arise). Of course, it’s not just rights that are at issue, but also responsibilities, particularly where data have privacy and ethical implications.
Curation service: Briefing papers on legal, ethical and licence issues.
Meanwhile a critical step is establishing the IT infrastructure. While the focus is likely to be on acquiring and testing the data acquisition and analysis equipment, software, capabilities and workflows, in practice the apparently background activities of ensuring adequate data storage, sensible disaster recovery procedures (or at the very least, proper data backup and recovery), and thoughtful data curation processes will be critical for later re-use.
Curation service: Briefing paper on Keeping your data safe?
Curation service: tools for various tasks, eg data audit, risk assessment
It’s worth remembering that there are different kinds of data storage. While you will often be most concerned with temporary or project-lifetime storage, you may need to think about storage with greater persistence. There may be a laboratory or institutional or subject repository, external database or data service that will be critical to your project; negotiating with the curation service providers associated with these services should be an important early step. In effect, right at the start you are planning for your exit! But more on this another time…
Curation service: Guidance on data repositories for projects?
Curation service: thought-stimulating discussion papers, blog posts and case studies, journal, conference, discussion fora…
Project execution
Through the lifetime of your project, your focus will rightly be on the research itself. But you should also take time out to test your disaster recovery procedures, and check on your quality assurance. You care about the quality of your materials and syntheses (or your algorithms, or your documentary sources), you need to care as much about the quality of your data, both as-observed and as-processed. You’ll be collecting a lot of data; if some support your hypothesis and some contradict it, you’ll need to ensure you are collecting (perhaps in laboratory notebooks or equivalent) sufficient provenance and context to justify the selection of the former rather than the latter.
Not sure if there is a curation service here, or if these are good practice research guides for your particular domain… but I guess the same could be said for standards and vocabularies. Question is whether domains HAVE good practice guides for data quality?
As the project goes forward, issues may arise in your original data management plan, and you should take the time to refine it (and document the changes), and your associated curation processes. If you did take idiosyncratic decisions in haste at project establishment, at some point you may need to review them. It’s a warning sign when a staffing change is followed by proposals for major architectural changes!
Finally, at some stage you should test any external deposit procedures built into your plan.
Curation service: Guidance on data deposit…
I don’t think I’m claiming that this is the limit of curation issues during project execution; rather that here the course of projects is so varied that generalisations are even less justifiable than elsewhere. Nevertheless, the analysis here should be strengthened, so input would be helpful!
Project termination stage
As the project comes towards an end, you enter another risk phase. Firstly, most management focus is likely to be on the next project (or the one after that!). Secondly, if employment is fixed term, tied to the particular project, then research staff will have been looking for other posts as the end of the project draws near, and the project may start to look denuded (and the data management and informatics folk may be the most marketable, leaving you exposed). And of course, the final reports etc cannot be written until the project is finished, by which time the project’s staffing resources will have disappeared. It’s the PI’s job to ensure these final project tasks are properly completed, but it’s not surprising that there are problems sometimes!
Curation service: Guidance on (or assistance with) evaluation
During this final phase, you must identify any data resources of continuing value (this should have occurred earlier, but does need re-visiting), and carry out the plans to deposit them in an appropriate location, together with the necessary contextual and provenance information. Easy, hey? Perhaps not: but failure here could have a negative impact on future grants, if research funders do their compliance checking properly.
Curation service: Guidance on appraisal, Guidance on data deposit.
Finally, given that data management plans are currently in their infancy, it’s worth commenting on the plan outcomes in your project final report!
Writing papers
This is not really a project phase as such, as often several papers will be produced at different stages of the project. The key issues here are:
- Include supplementary data with your paper where possible
- Ensure data embedded in the text are machine readable
- Cite your data sources
- Ensure supportive data are well-curated and kept available (preferably accessible; this is where a repository service may come in useful).
Curation service: Proposals for data citation
Curation service: Suggestions for the Well-supported article
So: the message is that curation should be a constant theme, albeit as background during most of the project execution. But the decisions taken during pre-proposal, proposal and establishment phases will have a big effect on your ability to curate your data, which may affect your research results, and will certainly affect the quality and re-usability of the data you deposit.
Carlson, S. (2006, June 23). Lost in a Sea of Science Data: Librarians are called in to archive huge amounts of information, but cultural and financial barriers stand in the way. The Chronicle of Higher Education, 52, 35.
Tuesday, 14 October 2008
ARROW Repositories day: 1
I’ve been giving a talk about the Research Repository System ideas at the ARROW repository day in Brisbane, Australia (which is partly why there has been a gap in posting recently). Here are some notes on the other talks.
Kate Blake from ARROW is talking about metadata. Particularly important for data, which cannot speak for itself. Metadata thought of as a compound object that comprises some parts for “library management” issues (things like author, title, keyword) for the whole document and/or its parts, plus University management parts, such as evidence records for research quality management purposes. These link to metadata that applies to the community of practice, eg the appropriate metadata for an X-ray image. Have the content (maybe a PDF), its rich metadata (Kate used MARC/XML as an example, which surprised me, since she also suggested this group was specific to the content), lightweight descriptive metadata, technical metadata (file size, type etc), administrative metadata, eg rights or other kinds of institutional metadata, preservation metadata such as PREMIS, and both internal and external relationship metadata. METS is one way to wrap this complex set of metadata and provide a structural map (there are others). (Worrying that this seems like a very large quantity of metadata for one little object…) Aha, she’s pointing out that aggregating these into repositories and these repositories together across search services leads ot problems of duplication, inconsistency, waste of effort, etc. So lots of work trying to unravel this knot, lots of acronyms: RDF, FRBR, SWAP, DCMI AM, SKOS etc…
FRBR making the distinction between the work, its expressions, manifestations and items. SWAP being a profile for scholarly works, as text works, not much use for data.
Names appear in multiple places in the metadata, and different parts have different rules. Do we have a name agent service (registries)? Need services to gather metadata automatically, that way you might introduce consistency and interoperability.
Kylie Pappalardo from QUT’s OAK Law project on legal issues on managing research data so it can be included in a repository and accessed by others. Government statements in favour of openness (eg Carr: More than one way to innovate, also “Venturous Australia” strategy). To implement these policies we need changes to practice and culture, institutional engagement, legal issues being addressed, etc. Data surrounded by law (!): copyright, contract, patents, policies, confidentiality, privacy, moral rights. Conflicting legal rights: who can do what with the data? QUT has OAK Law and also Legal Framework for e-Research project.
Survey online, May 2007, 176 participants responded. 50 depositing data in database; of those 46% said available openly, 46% required some or complete restrictions. 54% said their organisation did NOT have a data policy at all; where they did have a policy, most were give guidelines. 55% said they prepared plans for data management; two thirds of these at time of proposal, balance later. Should be early, not least because data management costs and should be part of the proposal, also disputes can be hard to resolve later. 7% felt that clearer info on sharing and re-use would help, and 90% wanted a “plain English” guide (who wouldn’t?). Lawyer language doesn’t help, so researchers make their own informal agreements… maybe OK if nothing goes wrong.
The group has a report: analysis of Legal Context of Infrastructure for Data Access and Re-use in Collaborative Research. Also Practical Data Management: a Legal and Policy Guide. They have some tools, including a “simple” Data Management (legal) toolkit to fill in to gather information about (eg) copyright ownership etc.
Peter Sefton of USQ talking about OAI-ORE, and what it can do for us. Making the point that we build things from a wide variety of standard components, which (mostly) work pretty well together, eg found bricks in a garden wall… OAI-PMH mostly works, moving metadata from one place to another. But it’s just the messenger, not the message. So a harvest of metadata across multiple repositories shows wide variations in the keywords, subjects etc. Problems with XACML for defining access policies: no standardisation on the names of subgroups, so in the end it’s no use for search. Point being that these standards may appear important but not work well in practice.
So on to ORE (Object Re-use and Exchange)… Pete asks “shouldn’t it be exchange, then re-use?”. ORE view of a blog post: a resource map describes the post, but in fact it’s an aggregation (compound object) of HTML text, a couple of images, comments in separate HTML, etc. The aggregation does have a URI, but does not have a fetchable reality (the resource map does). Can get complex very rapidly. See Repository Challenge at OpenRepositories 2008 in Southampton, and the ORE Challenge at RepoCamp 2008. USQ participating with Cambridge in JISC-funded TheOREM project, also internal image project called The Fascinator. Based on his ICE system that has been mentioned before, integrated with ORE tools to push stuff into the repository. Can have the repository watching so it can get it for itself.
ORE can: supplement OAI-PMH for moving content around; improve research tols like Zotero; replace use of METS packages; allow “thesis by publication” more elegantly; and pave the way for a repository architecture that understands content models (no more discussion of atomistic versus compound objects).
Kate Blake from ARROW is talking about metadata. Particularly important for data, which cannot speak for itself. Metadata thought of as a compound object that comprises some parts for “library management” issues (things like author, title, keyword) for the whole document and/or its parts, plus University management parts, such as evidence records for research quality management purposes. These link to metadata that applies to the community of practice, eg the appropriate metadata for an X-ray image. Have the content (maybe a PDF), its rich metadata (Kate used MARC/XML as an example, which surprised me, since she also suggested this group was specific to the content), lightweight descriptive metadata, technical metadata (file size, type etc), administrative metadata, eg rights or other kinds of institutional metadata, preservation metadata such as PREMIS, and both internal and external relationship metadata. METS is one way to wrap this complex set of metadata and provide a structural map (there are others). (Worrying that this seems like a very large quantity of metadata for one little object…) Aha, she’s pointing out that aggregating these into repositories and these repositories together across search services leads ot problems of duplication, inconsistency, waste of effort, etc. So lots of work trying to unravel this knot, lots of acronyms: RDF, FRBR, SWAP, DCMI AM, SKOS etc…
FRBR making the distinction between the work, its expressions, manifestations and items. SWAP being a profile for scholarly works, as text works, not much use for data.
Names appear in multiple places in the metadata, and different parts have different rules. Do we have a name agent service (registries)? Need services to gather metadata automatically, that way you might introduce consistency and interoperability.
Kylie Pappalardo from QUT’s OAK Law project on legal issues on managing research data so it can be included in a repository and accessed by others. Government statements in favour of openness (eg Carr: More than one way to innovate, also “Venturous Australia” strategy). To implement these policies we need changes to practice and culture, institutional engagement, legal issues being addressed, etc. Data surrounded by law (!): copyright, contract, patents, policies, confidentiality, privacy, moral rights. Conflicting legal rights: who can do what with the data? QUT has OAK Law and also Legal Framework for e-Research project.
Survey online, May 2007, 176 participants responded. 50 depositing data in database; of those 46% said available openly, 46% required some or complete restrictions. 54% said their organisation did NOT have a data policy at all; where they did have a policy, most were give guidelines. 55% said they prepared plans for data management; two thirds of these at time of proposal, balance later. Should be early, not least because data management costs and should be part of the proposal, also disputes can be hard to resolve later. 7% felt that clearer info on sharing and re-use would help, and 90% wanted a “plain English” guide (who wouldn’t?). Lawyer language doesn’t help, so researchers make their own informal agreements… maybe OK if nothing goes wrong.
The group has a report: analysis of Legal Context of Infrastructure for Data Access and Re-use in Collaborative Research. Also Practical Data Management: a Legal and Policy Guide. They have some tools, including a “simple” Data Management (legal) toolkit to fill in to gather information about (eg) copyright ownership etc.
Peter Sefton of USQ talking about OAI-ORE, and what it can do for us. Making the point that we build things from a wide variety of standard components, which (mostly) work pretty well together, eg found bricks in a garden wall… OAI-PMH mostly works, moving metadata from one place to another. But it’s just the messenger, not the message. So a harvest of metadata across multiple repositories shows wide variations in the keywords, subjects etc. Problems with XACML for defining access policies: no standardisation on the names of subgroups, so in the end it’s no use for search. Point being that these standards may appear important but not work well in practice.
So on to ORE (Object Re-use and Exchange)… Pete asks “shouldn’t it be exchange, then re-use?”. ORE view of a blog post: a resource map describes the post, but in fact it’s an aggregation (compound object) of HTML text, a couple of images, comments in separate HTML, etc. The aggregation does have a URI, but does not have a fetchable reality (the resource map does). Can get complex very rapidly. See Repository Challenge at OpenRepositories 2008 in Southampton, and the ORE Challenge at RepoCamp 2008. USQ participating with Cambridge in JISC-funded TheOREM project, also internal image project called The Fascinator. Based on his ICE system that has been mentioned before, integrated with ORE tools to push stuff into the repository. Can have the repository watching so it can get it for itself.
ORE can: supplement OAI-PMH for moving content around; improve research tols like Zotero; replace use of METS packages; allow “thesis by publication” more elegantly; and pave the way for a repository architecture that understands content models (no more discussion of atomistic versus compound objects).
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