Introduction to Data Management Planning at Alien Challenge COST workshop
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Data Management Planning (DMP): what, why, how?
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Introduction to Data Management Planning at Alien Challenge COST workshop
- 1. Introduction to Data Management Planning Aaike De Wever @aaik
- 2. Landscape with the Fall of Icarus, Royal Museums of Fine Arts of Belgium, now seen as a good early copy of Bruegel's original - ca. 1558 - By Pieter Brueghel the Elder (1526/1530–1569) - 1., Public Domain, https://commons.wikimedia.org/w/index.php?curid=11974918
- 3. Source: JISC http://webarchive.nationalarchives.gov.uk/20140702233839/http:/www.jisc.ac.uk/whatwedo/campaigns/res3/jischelp.aspx Research Life Cycle and the Data Life Cycle
- 5. Data decay Vines et al. (2014) The Availability of Research Data Declines Rapidly with Article Age. Current Biology 24, 1-4.
- 6. “Data often have a longer lifespan than the research project that creates them. Researchers may continue to work on data after funding has ceased, follow-up projects may analyse or add to the data, and data may be re-used by other researchers.” “Well organised, well documented, preserved and shared data are invaluable to advance scientific inquiry and to increase opportunities for learning and innovation.” Source: http://www.data-archive.ac.uk/create-manage/life-cycle
- 7. Data management plan: definition A data management plan or DMP is a formal document that outlines how you will handle your data both during your research, and after the project is completed. The goal of a data management plan is to consider the many aspects of data management, metadata generation, data preservation, and analysis before the project begins; this ensures that data are well- managed in the present, and prepared for preservation in the future.
- 8. Motivation to construct a DMP • Intrinsic motivation • Obligation from: • institute • funder
- 9. What should motivate you (1/2) • Planning facilitates later work, esp. with regards to: • data storage • publication • archival & retrieval • Opportunity to consider how you handle data, incl.: • recording new data • efficient organisation of data • workflows to ensure its quality
- 10. What should motivate you (2/2) • Ensuring data is understandable and re-usable, thus: • maximising the visibility & impact • consider data publication, re-use and dissemination of data and results at an early stage • Opportunity to “talk data” with project partners, covering e.g.: • data workflows • data exchange • common data management policies and practices
- 11. Approach for constructing DMP depends on • Purpose –Project proposal –Improving internal data management –Overall open data policy • Addressee –Funding agency: NSF, EU,… –Internal / Website • Type(s) of data and volume –Big data such as next generation sequencing output or automatic sensor data –Remote sensing imagery –Biodiversity monitoring campaign
- 12. Typical components of a DMP What Info Standards Sharing Storage
- 13. Typical components of a DMP What data and analyses are expected Info about data (metadata) Standards for data storage and exchange Sharing, publication, public archiving Storage preservation and access to data
- 14. DMP components: Simple example Data collected during runs: distance, duration, speed, cadence, heartrate
- 15. DMP components: Simple example Data collected during runs: distance, duration, speed, cadence, heartrate Personal info on runner entered when configuring device/account Metadata for run automatically recorded (date, time) or associate during data upload (weather) Additional metadata for run (shoes, remarks) added through web interface Raw data in .fit format Uploaded data available through web services (json format) for exchange with other services Summary data downloadable as csv-data Data on device (as long as storage permits) and pending uploads on computer Uploaded data accessible through on-line service Ad-hoc download of summary data on computer (end of year) Data accessible to authorised “connections” Privacy settings can be set for individual activities
- 16. DMP components: Sequence data Data from 4 454-sequencing runs Expected maximum file size: 4 Gb raw data/run Output format: Standard Flowgram Format (SFF) Information in ReadMe-file: • Project details & background • Information on sample origin & selection • Sequencing methodology Raw data in: Standard Flowgram Format (SFF) FASTA format for analysis and storage Metadata included in FASTA format • Local storage on removable HD • Back-up through cloud storage • Raw data in NCBI Sequence Read Archive • Annotated FASTA data in EMBL/GenBank • Data associated with publications in Datadryad.org
- 17. DMP components: Monitoring data Data from monthly water quality monitoring at 100 stations, 15 parameters recorded in spreadsheet (data volume 194 kb in xlsx, 62 in csv) Details of individual sampling events recorded in spreadsheet Sampling & analysis protocol recorded in field manual Spreadsheet data imported into SQL database with export queries for: • Occurrence data in csv using fields from Darwin Core standard • Metadata in Ecological Metadate Language (EML) • Local storage: Raw spreadsheet data on field operators’ device • Database on institute servers + local back-ups • Occurrence data through GBIF on national IPT node • Data associated with publications in Datadryad.org
- 18. Funders
- 19. What Info Standards Sharing Storage Typical components of a DMP for NSF-Bio • media and methods • policies and public access 1. Describe data, metadata, formats standards 2. Physical/cyber resources and facilities 3. Media and dissemination methods 4. Policies for data sharing and public access 5. Roles and responsibilities & Who
- 20. H2020 – Pilot action Pilot action on open access to research data – Research Data generated by the project: • deposit in a research data repository and take measures to make it possible for third parties to access, mine, exploit, reproduce and disseminate — free of charge for any user — the following: • the data, including associated metadata, needed to validate the results presented in scientific publications as soon as possible; • other data, including associated metadata, as specified and within the deadlines laid down in the data management plan (see Annex I);
- 21. Typical components of a DMP for H2020 What Info Standards Sharing Storage 1. What types of data will the project generate/collect? 2. What standards will be used? 3. How will this data be exploited and/or shared/made accessible for verification and re- use? If data cannot be made available, explain why. 4. How will this data be curated and preserved?
- 22. H2020 DMP example: AQUACROSS Knowledge, Assessment, and Management for AQUAtic Biodiversity and Ecosystem Services aCROSS EU policies - aquacross.eu: • 16 partners • 8 case studies (over 4 WPs) • dataset survey • … case studies to be worked out, datasets to be used only partially known Organisation of Data Management Plan • Overall DMP distinguishes categories of data (but has to remain general) • Allows for more specific DMP at case study level • Living document, updating at regular intervals
- 23. Open data institutes in biodiversity monitoring? See https://www.inbo.be/en/norms-for-data-use
- 24. Open data institutes in biodiversity monitoring? Key actions: - All data owned by the institute are covered by open data policy - Data are released after an embargo period of 12 months - Focus on raw data (biodiversity, sequence and map data) - Data associated with scientific papers are opened up - Data are released under the CC0 license waiver - Data norms apply - Data are sufficiently documented through metadata - All research projects are required to prepare a DMP - Researches are required to apply these policies - Researchers are supported by the institutes data unit
- 25. Features specific for Alien Invasive Species data? • Need to integrate initiatives in regional and national networks (Crall et al. 2010 doi:10.1007/s10530-010-9740-9) • Need for faster sharing of information (for early warning and rapid response) • More thorough data validation measures (before taking management action) • Link observation data with data on management response (common standard for recording this?) • Concerns about privacy
- 26. The Tower of Babel (1563), Kunsthistorisches Museum, Vienna, oil on board - By Pieter Brueghel the Elder (1526/1530–1569) - 1., Public Domain, https://commons.wikimedia.org/w/index.php?curid=11974918
- 28. Background material Digital Data Curation Centre • http://www.dcc.ac.uk/resources/how-guides/develop-data-plan Library University of Michigan • http://www.lib.umich.edu/research-data-services/data-management- planning (and http://www.lib.umich.edu/research-data-services/nsf- data-management-plans#examples_proposals for sample plans) Data One • https://www.dataone.org/best-practices
- 29. Tools DMP Online - https://dmponline.dcc.ac.uk DMPTool - https://dmptool.org Others: • DMP builder - https://dmp.library.ualberta.ca • DMP editor - http://www.openmetadata.org/site/?page_id= 373 • IEDA Data Management Plan tool - http://www.iedadata.org/compliance/plan
- 30. What data and analyses are expected? Describe the data to be collected (actual observations) during your research including amount (if known). Name the type of data, the instrument or collection approach, and how the data will be sampled. If actual data are interpreted, note the interpretation. Describe any quality control measures. Also describe the final derivative products (datasets and software or computer code) and the analysis used including analytical software packages that are required for replication, etc. Describe data both (digital and analog) and physical materials (samples and collections) gathered or generated during the tie of the award. Consider these questions: • What data will be generated in the research? • What data types will you be creating or capturing? (e.g. experimental measures, qualitative, raw, processed) • How will you capture or create the data? (This should cover content selection, instrumentation, technologies and approaches chosen, methods for naming, versioning, meeting user needs, etc, and should be sensitive to the location in which data capture is taking place.) • If you will be using existing data, state that fact and include where you got it. What is the relationship between the data you are collecting and the existing data? Source: https://dmptool.org
- 31. Info/Standards – Standards, Formats and Metadata Describe the format of your data; think about what details (metadata) someone else would need to be able to use these files. Describe the structural standards that you will apply in making data and metadata available. For example, for most ecological data, documentation should be structured in Ecological Metadata Language (EML). An example of metadata could also be as simple as a "readme file" to explain variables, structure of the files, etc. Consider these questions: • Which file formats will you use for your data and why? • What form will the metadata describing/documenting your data take? • How will you create or capture these details? • Which metadata standards will you use and why have you chosen them? (e.g. accepted domain-local standards, widespread usage) • What contextual details (metadata) are needed to make the data you capture or collect meaningful? Source: https://dmptool.org
- 32. Who – Roles and responsabilities Explain how the responsibilities regarding the management of your data will be delegated. This should include time allocations, project management of technical aspects, training requirements, and contributions of non- project staff - individuals should be named where possible. Remember that those responsible for long-term decisions about your data will likely be the custodians of the repository/archive you choose to store your data. While the costs associated with your research (and the results of your research) must be specified in the Budget Justification portion of the proposal, you may want to reiterate who will be responsible for funding the management of your data. Consider the following: • Outline the staff/organizational roles and responsibilities for implementing this data management plan. • Who will be responsible for data management and for monitoring the data management plan? • How will adherence to this data management plan be checked or demonstrated? • What process is in place for transferring responsibility for the data? • Who will have responsibility over time for decisions about the data once the original personnel are no longer available? Source: https://dmptool.org
- 33. Sharing – Dissemination methods Describe how and where you will make these data and metadata available to the community. Remember BIO is committed to timely and rapid data distribution; make sure you address how soon your data will be available. Indicate what data will be made available and preserved. Will data be accessible on a web page, by email request, via open-access repository, etc.? Consider these questions: • What data will be made available from the study and preserved for the long-term? • How and when will you make the data available? (Include resources needed to make the data available: equipment, systems, expertise, etc.) • What transformations will be necessary to prepare data for preservation / data sharing? • What metadata / documentation will be submitted alongside the data or created on deposit/ transformation in order to make the data reusable? • What related information will be deposited? • What is the process for gaining access to the data? • How long will the original data collector/creator/principal investigator retain the right to use the data before opening it up to wider use? • Explain details of any embargo periods for political/commercial/patent or publisher reasons. Source: https://dmptool.org
- 34. Sharing – Policies for Data Sharing and Public Access Describe the policies under which these data will be made available. It is very important, the reason a DMP is required, that you specify how you will share your data with non-group members after the project is completed. If the data is of a sensitive nature—privacy or ecological endangerment concerns, for instance— and public access is inappropriate, address here the means by which granular control and access will be achieved (e.g. formal consent agreements, anonymized data, only available within a secure network, etc.). Consider these questions: • Will any permission restrictions need to be placed on the data? • Are there ethical and privacy issues? If so, how will these be resolved? • What have you done to comply with your obligations in your IRB Protocol? • Who will hold the intellectual property rights to the data and how might this affect data access? • What and who are the intended or foreseeable uses/users of the data? • Do you plan on publishing findings which rely on the data? If so, do your prospective publishers place any restrictions on other avenues of publication? Source: https://dmptool.org
- 35. Storage – Archiving, Storage and Preservation Consider which data (or research products) will be deposited for long-term access and where. (What physical and/or cyber resources and facilities (including third party resources) will be used to store and preserve the data after the grant ends?) Describe your long-term strategy for storing, archiving and preserving the data you will generate or use. Consider the following: • What is the long-term strategy for maintaining, curating and archiving the data? • Which archive/repository/database have you identified as a place to deposit data? • What procedures does your intended long-term data storage facility have in place for preservation and backup? • How long will/should data be kept beyond the life of the project? • What data will be preserved for the long-term? • On what basis will data be selected for long-term preservation? • What metadata/documentation will be submitted alongside the data or created on deposit/transformation in order to make the data reusable? • What related information will be deposited? Source: https://dmptool.org
Editor's Notes
- Aaike De Wever - Royal Belgian Institute of Natural Sciences - involved in a wide range of initiatives around freshwater biodiversity data (management)
- Landscape with the Fall of Icarus (originally by Pieter Brueghel the Elder Data is the "soil" of a lot of the work we do nowadays. ... and we should be tilling it, "managing it" ... and because it can be labour intensive, we tend to resist doing it ... moreover, we need to do this at the right time! This is where planning comes in.
- Data management is an essential component of the research life cycle. In this image, the data life cycle is considered part of the “research process”. There are obviously a lot of ways to depict the data life cycle, but this one [from the UK data archive] …
- [There are obviously a lot of ways to depict the data life cycle, but this one from the UK data archive] … provides a very good overview. Most of us cover at least a few of these steps; "Create”, “Process”, “Analyse” But are maybe less active or less concerned about “Presering data”, “Giving access and “Re-using it”
- But, if we fail to plan what we will do with our data, the majority will go lost over time.
- ... and that is a pity. Because often data can be (re)used in many more ways than we imagine. Which is nicely illustrated by this quote: “Data often have a longer lifespan than the research project that creates them. Researchers may continue to work on data after funding has ceased, follow-up projects may analyse or add to the data, and data may be re-used by other researchers. Well organised, well documented, preserved and shared data are invaluable to advance scientific inquiry and to increase opportunities for learning and innovation.”
- This is where Data Management Plans come in. Getting a plan on paper helps to carry it out, stick to it, refer to it. So, what are Data Management Plans? A Data Management Plan is a formal document that outlines how you will handle your data [both during and after your project]. The goal of a data management plan is to consider the many aspects of data management, metadata generation, data preservation, and analysis before the project begins; and ensure good management both in the present and future.
- The motivation for compiling a data management plan can be manifold, but very roughly you could either do so because you see its value or because you are required to do so by your institute or funder.
- Nevertheless, I believe most researchers could benefit from engaging in data management planning, because: 1) (as with the tilling) planning makes things easier in the future [for yourself] 2) it is an opportunity to reflect on the way you handle and organise data,
- [towards the outside world] 3) and how you make it visible, and available for re-use 4) It is also an excellent opportunity to "talk data" with your project partners, clarify workflows, exchanges and so on.
- Regardless of the approach you take to Data Management Planning, there are a number of typical components to consider; I’d like to call this the Wisdom –with tripple “s”- of data management planning - What - Info - Standards - Sharing and - Storage
- Under “What” I consider expected data and planned data analyses By “Info” I cover the information about data –also known as metadata– and documentation that is necessary for others to discover and interpret the data correctly Under “Standards” I refer to data formats and standards that allow data to be exchanged within the community By “Sharing” I mean the act of making data available for re-use through on-line data publication or public archiving … and ”Storage” covers both the physical storage, back-up plans and long term preservation.
- Next Generation Sequencing study: as we needed to get a small description on paper which covered a few of these topics it was somewhat of a Data Management Plan “avant là lettre”. So, for “what”, we were anticipating a number of 454-runs, with a maximum filesize of 4 Gb per run in the SFF-format. Metadata would be recorded in a ReadMe file. Data would be exported in FASTA-files including metadata. Raw data could be stored on the NCBI Sequence Read Archive, annotated sequences would go into (one of) the International Nucleotide Sequence Database Collaboration http://www.insdc.org. Potentially selected data used in publications could be archived in Dryad. In terms of storage, a local (removable) HD sufficed and back-ups could still be done through a kind of cloud storage.
- I have also included a very much simplified example for monitoring data: Say we consider a monthly monitoring campaign at 100 stations during which we record 15 parameters. The data volume is very manageable on local storage. Metadata for the sampling event are recorded in the excel file and sampling details are recorded in field manuals. Spreadsheet data are imported in the institute’s SQL database with export queries for occurrences in Darwin Core and metadata in the EML Ecological Metadata Language-format Occurrence data can be published through the GBIF network and data associated with papers can (additionally) be archived through Dryad Local storage typically takes place on personal computers and field operators’ devices, while the database is stored on the institutes servers. Obviously, in reality each of the 15 parameters (such as chlorophyll analysis, macro-invertebrate count,…) could represent a separate analysis step, which later requires integration in a central database. Documenting these workflows would be part of the DMP.
- But as I mentioned earlier, funders have also discovered Data Management Plans, which is probably one of the reasons why this is becoming a hot topic. If I am not mistaken, NSF in the US is requiring DMP since 2011, The EU currently has a pilot phase running which requires selected projects to produce a Data Management Plan and make their data publicly available... ... and other funders are following their example. But each of these funders have slightly different requirements for the DMPs they request.
- The NSF for instance requires it during project application. The biggest difference with what we discussed earlier is that they also request info on “Who” is doing the data management and distinguish between media and methods for data sharing and policies for doing so and public access. This results in these 5 categories, which overlap with one or more of the topics we discussed earlier. Since DMPs will be considered during the merit review process, to help reviewers, and as appropriate, please organize the DMP as follows: Describe the data that will be collected, and the data and metadata formats and standards used. Describe what physical and/or cyber resources and facilities (including third party resources) will be used to store and preserve the data after the grant ends. Describe what media and dissemination methods will be used to make the data and metadata available to others after the grant ends. Describe the policies for data sharing and public access (including provisions for protection of privacy, confidentiality, security, intellectual property rights and other rights as appropriate). Describe the roles and responsibilities of all parties with respect to the management of the data (including contingency plans for the departure of key personnel from the project) after the grant ends.
- The EU is currently running a pilot on open access data under its Horizon 2020 funding program. This pilot requires selected projects to deposit their data in repositories to make it available for re-use. They explicitly refer to the need to archive data associated with scientific papers and allow for a grace period for any “other data”.
- In terms of DMP they rather consider it as a living document from which different versions are produced during the project’s lifetime. The requirements for DMPs are described as follows: What types of data will the project generate/collect? What standards will be used? How will this data be exploited and/or shared/made accessible for verification and re- use? If data cannot be made available, explain why. How will this data be curated and preserved?