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1 | # | Use Case Description | Reference | Agriculture Data IG (IGAD) | Active Data Management Plans IG | Archives and Records Professionals for Research Data IG | Big Data IG | Biodiversity Data Integration IG | Brokering IG | Chemistry Research Data IG | Data Fabric IG | Data for Development IG | Data Foundations and Terminology IG | Data in Context IG | Data Rescue IG | Development of cloud computing capacity and education in developing world research IG | Digital Practices in History and Ethnography IG | Domain Repositories Interest Group | Education and Training on handling of research data | ELIXIR Bridging Force IG | Engagement IG | Ethics and Social Aspects of Data IG | Federated Identity Management IG | Geospatial IG | Global Water Information IG | Health Data | Libraries for Research Data IG | Long tail of research data IG | Marine Data Harmonization IG | Metabolomics Data Interoperability IG | Metadata IG | National Data Services | PID IG | Preservation e-Infrastructure IG | Quality of Urban Life IG | RDA/CODATA Legal Interoperability IG | RDA/CODATA Materials Data, Infrastructure & Interoperability IG | RDA/WDS Certification of Digital Repositories IG | RDA/WDS Publishing Data Cost Recovery for Data Centres IG | RDA/WDS Publishing Data IG | Repository Platforms for Research Data IG | Reproducibility IG | Research data needs of the Photon and Neutron Science community IG | Research Data Provenance | Service Management IG | Structural Biology IG | Vocabulary Services Interest Group | |
2 | Imma Subirats, Devika Madalli, Johannes Keizer | David Giaretta, Helen Glaves, Kevin Ashley | Elise Dunham, Rebecca Grant | Peter Baumann, Ben Evans, Kwo-Sen Kuo, Morris Riedel | Nicola Nicolson, Vince Smith, Paul Kirk, Dimitris Koureas | Stefano Nativi, Jay Pearlman | Leah McEwen, Stuart Chalk, Ian Bruno, David Martinsen, Richard Kidd | Alan Blatecky, Yunquiang Zhu, Peter Wittenburg | Ingvill C. Mochmann | Gary Berg-Cross, Raphael Ritz | Keith Jeffery, Rebecca Koskela | Elizabeth Griffin, David Gallaher, Lesley Wyborn | Hugh Shanahan, Andrew Harrison | Kim Fortun, Mike Fortun, Jason Baird Jackson | George Alter, Ruth E. Duerr, Robert J. Hanisch, Peter Doorn | Yuri Demchenko, Laura Molloy | Bengt Persson, Carole Goble, Rob Hooft | Inna Kouper, Andrew Maffei | Kalpana Shankar, Candice Lanius | Daan Broeder, Bob Jones | Suchith Anand, Peter Baumann, Luciene Delazari, Andrea Perego, Chris Pettit | Ilya Zaslavsky, Sylvain Grellet, Tony Boston, Matthew Fry | Wolfram Horstmann, Kathleen Shearer, Michael Witt | Kathleen Shearer and Wolfram Horstmann | Helen Glaves, Cyndy Chandler, Dawn Wright | Christoph Steinbeck, Shankar Subramanian, Susanna Sansone | Keith Jeffery, Rebecca Koskela | Kevin Ashley, Adrian Burton | weigel | David Giaretta, Jamie Shears, Ruth Duerr | Chris Pettit, Massimo Craglia, Piyushimita (Vonu) Thakuriah | Paul F. Uhlir, Enrique Alonso Garcia, Bob Chen | James A. Warren, Laura M. Bartolo | Micheal Diepenbroek, Ingrid Dillo, Mustapha Mokrane | Aita de Waard, Ingrid Dillo, Simon Hodson | Todd Carpenter, Micheal Diepenbroek, Eefke Smit, Jonathan Tedds, Mustapha Mokrane | David Wilcox, Stefan Kramer, Ralph Müller-Pfefferkorn | Bernard Schutz, Victoria Stodden | Amber Boehnlein, Brian Matthews, Frank Schlünzen, Thomas Proffen | David Dubin, Bridget Almas | Owen Appleton, Michael Brenner, Thomas Schaaf | Lucia Banci, Chris Morris, Antonio Rosato | Stephan Zednik and Simon Cox | |||||
3 | 1 | Cosmopolitan species physiological response and strain variability across ecological gradients | http://cmore.soest.hawaii.edu/rcn2015/files/UseCase_Alexander.pdf | accessing large genomics/transcriptomics databases based on environmental parameters/location | analyze sequence abundance | figure out queries against genomics databases based on location and related context | best practices for domain repositories to expose APIs that would enable location and context-based queries | how to ensure that all stakeholders are engaged to make the query possible | Explore best practices for publishing and accessing metabolomics data to enable queries by space/time and environmental context variables | common metadata elements across geospatial and metagenomics databases | make sure that taxonomies of organisms of interests are compatible across databases (or formally expressed so that mappings can be done) | |||||||||||||||||||||||||||||||||||||
4 | 2 | Diffuse nutrient leakage reporting to the Water Framework Directive | http://www.eurogeographics.org/documents/RISE18UseCaseDocumentV1.5.pdf | |||||||||||||||||||||||||||||||||||||||||||||
5 | 3 | Hydroshare Model Resource Use Case: understanding potential of stormwater controls in an urban watershed | https://github.com/hydroshare/hydroshare/wiki/Hydroshare-Resources-Use-Cases | Model programs and model input and output data are part of the data management. | Models hosted in the system bring them one step closer to automatically transferring to a high performance compute resource to support modeling with large datasets. | Focused on data and models structured for the hydrology and watershed sciences domain. | Can become a library for contributed hydrologic model instances (applications of models at a specific site). | Individual researchers can publish their models and model instances. | Includes a metadata model for model programs and model instances (applications of models at a specific site with input and output data) | Holds contributed hydrologic research data related to modeling. | Reference to specific model and the input data and parameters enhances reproducibility. | Reference to specific model and input data versions enhances provenance. Still somewhat rudimentary and could benefit being advanced. | ||||||||||||||||||||||||||||||||||||
6 | 4 | Hydroshare resource Publication Use Case | https://github.com/researchsoftwareinstitute/software-data-citation-ws/issues/11 | Engage users early in the research cycle so that metadata grows through evolution of the data resources, and through social media comment and rating additions. This is also the time researchers are interested in the data, and know it best for annotating it with correct metadata, rather than at the end of the project when it is a chore. | Use iRODS to work with datasets larger than could efficiently be handled on desktop computers. | Focused on data structured for the hydrology and watershed sciences domain. | Includes geographic raster, geographic feature and multidimensional space-time resource types. Supports automatic extraction and annotation of coverage metadata for these resource types. Supports map server visualization and applications acting on these resource types. | Focused on water data for the hydrologic and watershed sciences domains. | Can become a library for contributed hydrologic research data. | Individual researchers can publish their data. | Strives to balance metadata required for proper interpretation with chasing users away. Could benefit from advances in this area. | Can become a platform for contributed hydrologic research data. | Enables publication of data and models along with research products to enhance reproducibility. | Facilitates versioning and referencing among resources to enhance provenance. Still somewhat rudimentary and could benefit being advanced. | ||||||||||||||||||||||||||||||||||
7 | 5 | CINERGI discovery use case 1: Global rivers: major dissolved anion and cation concentrations | https://github.com/CINERGI/UseCases/issues/5 | interactions of dissolved cations and anions in water, and agriculture: accumulation of ions in crops; appropriate water quality for irrigation; concentrations as influenced by agricultural runoff | how would this use case be captured in DMPs given the many dependencies | potential re-use of chemical ontologies to find "major dissolved ions" (in Chebi?) | how such queries can be cast in foundational terms so that we can use generic query templates | context and spatial joins? | measurements like this may be done in multiple smaller projects and are at risk | which domain repository data need to be integrated to respond to this query, and how access and other policies, and lifecycles, coordinated | efficient parsing spatial join parameters from such a query, and executing it in a distributed system of services | this appears a typical long tail use case: small projects, poorly connected, with questionable sustainability, data retention, lifecycle management, with compatible data in publications and not in managed datasets. Transition guidelines? | Sufficient metadata to make this query possible across repositories? | given the "long tail" and dynamic nature of data in this query, what are reproducibility criteria and guidelines | answering the query implies a workflow with provenance recorded | a common issue with long tail data is staging them as services for such queries. How we then manage this service lifecycle | Several ontologies needed: chemical parameters, water quality, coastal features, hydrologic features, samples. | |||||||||||||||||||||||||||||||
8 | 6 | CINERGI discovery use case 2: CZO: bedload tracer experiments in the field and ralated studies | https://github.com/CINERGI/UseCases/issues/1 | |||||||||||||||||||||||||||||||||||||||||||||
9 | 7 | W3C Spatial: Time Series of a Water Course | https://www.w3.org/2015/spatial/wiki/Working_Use_Cases#Images.2C_e.g._a_Time_series_of_a_Water_Course_.28Best_Practice.2C_Time.2C_SSN.2C_Coverage.29 | Depending on the geographical scope and community time series and watercourse can be exposed using different formats. Cross border issues | Need reference datasets for watercourses | Metadata on timeseries ? Watercourse datasets | How to steer national water data services into using the same IT approach / standards | PID of watercourse, timeserie | Timeseries of something need controlled, shared vocab | |||||||||||||||||||||||||||||||||||||||
10 | 8 | W3C Spatial: droughts in geologically complex environments where groundwater is important | https://www.w3.org/2015/spatial/wiki/Working_Use_Cases#Droughts_in_geological_complex_environments_where_groundwater_is_important_.28Best_Practice.2C_Time.2C_SSN.2C_Coverage.29 | Hydrogeology data models could be highly complex from an IT point of view. Some are gridded. need for guidance, harmonisation on their storage and the way to querry them | Depending on the geographical scope and community the information can be exposed using different formats. Cross border issues | Hydrogeology models are fine tuned, often maybe by scientist using various tools. Challenge is how to create, update them based on new information coming in ? (remember it is underground so we often can only infer behaviour of the system) | Need reference datasets for surface, ground water (and theiur geospatial connexion!) + monitoring facilities | Simply having a metadata catalogue describing which hydrogeological model is avaible, where, its resolution, why it was created, using which tool is a plus. Additionnal information is also crucial to be sure the models are re-used the right way, and the associated uncertainty/biases they vehiculate | Hydrogeology models are often tied to software used to generate them. How can 2 National Water Data Services can share their respective model on the same ressource | PID of hydrogeological model | Sharing models and their in/outputs needs controlled, shared vocab | |||||||||||||||||||||||||||||||||||||
11 | 9 | NFIE: concept of reach-catchment pairs, uniquely identified at high resolution, to enable real-time vector-based streamflow routing | NFIE: https://www.cuahsi.org/NFIE, RAPID: http://rapid-hub.org/ | This concept directly supports flood forecasting and mapping. The Iowa Flood Center has mapped 3 million reaches and catchments in their state alone. | Models and workflow for estimating wide-area streamflow and flooding based on weather forecasts can be taught / learned in a 2-3 day workshop. Reach-catchment networks are not normally captured in most countries, but can be determined from 1:100k flowline network and 30m (or better) elevation models. | |||||||||||||||||||||||||||||||||||||||||||
12 | 10 | Data sharing to support transboundary integrated water resources management | http://www.tandfonline.com/doi/full/10.1080/02508060.2015.981783 | |||||||||||||||||||||||||||||||||||||||||||||
13 | 11 | Relevant data and parameters for understanding and validating models and comparing observations about water reservoirs and lakes, with specific emphasis on addressing algae bloom. | https://grid.ifca.es/wiki/ModellingWaterReservoirs | We are also in contact with this IG for the integration of ontologies | We are in collaboration with EGI.eu and INDIGO DataCloud to enable the prostprocessing of large output files from Delft3D simulations, including how to find the optimal way to explore parameters and find best matching | In contact for the algae bloom characteristics (in particular expression, as different species and also different harmful impact, like toxins, etc) | This is it. | Similar reflection regarding Active Management IG | We work with the National Research Council in Spain as a relevant example | Preservation Portal | Preservation portal includes launching VM for exact analysis reproduction | |||||||||||||||||||||||||||||||||||||
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