Match data set availability to data resource requirements, including gap analysis and remediation assistance.

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1 Discovering data/datasets Specify Data Requirements Identify Data Assets Assist customers with clarifying problem statements, use cases, high-level requirements (e.g. goals, objectives) and detailed requirements (e.g., tasks and deliverables needed to realize problem solution). Match data set availability to data resource requirements, including gap analysis and remediation assistance. Navigate institutional data assets Navigate the institution's data assets (with meaning, sourcing and other relevant information) over a longitudinal period. Understanding and exploring data/datasets Source data model and data definition Answer questions beyond the content contained in existing data set documentation. Concept mapping over time across successor systems Concept mapping from operational data to research concepts Self-service cohort identification Provide expertise in the methods by which the same concepts' data resources should be extracted from disparate (successor) source systems. Provide expertise in the methods by which the same concepts are instantiated between data resources. Providing researchers self-service tools to search and explore an existing database. Consultative cohort identification Consultation services to help researchers define and build a cohort.

2 Enabling access to institutional data/datasets Access to Health System Data Warehouse Access to Reseach Data Warehouse Provide technical protocol for accessing HSDW resources. Provide technical protocol for accessing RDW resources. Access to Clinical Data Repository Provide technical protocol for accessing CDR resources. Access to Clarity Provide technical protocol for accessing Clarity resources. Access to real-time Clinical Data Provide technical protocol for accessing real-time clinical data resources. Access to ITS Data Warehouse Access to Publication Citations Provide technical protocol for accessing ITS Data Warhouse resources, including Teaching and Learning, Student, Finance, Research Administration, Human Resources, Payroll, and Physical Resources subject areas. Provide technical protocol for accessing Publications resources. Access to Registries Provide technical protocol for accessing registry resources. Data management/data sharing plans

3 Writing data management/data Provide training and consultations on best practices for data management plans. sharing plans Data management/data sharing plans: tools and templates Provide tools, templates, sample language for data management plans. Identification of relevant standards Metadata for curation, discoverability Providing information about funding agencies' requirements for data management and data sharing Planning for Data in Research Planning Consultations for Data Management Enabling access to institutional metadata resources Dataset Catalog Central provisioning of information on data set resources, for data sets (internal or external) deemed by their stewards to be of interest to the Michigan Medicine community. Information Glossary Information on terms (such as definition, stewardship, data sources, algorithms and transformations) as they are utilized by Michigan Medicine constituents. Data Models Data models describe the design of data structures, including all elements and the relationships between them, diagrammatically.

4 Data Definitions For the assurance of the proper use and interpretation of data resources across user communities, data definitions specify, describe, explain and clarify the meaning of data. Data Lineage Data Dictionaries Data Lineage explains the processes by which data is created, read, updated and deleted. These provide the necessary level of support for user communities to understand the ordered steps by which data is extracted, transformed and loaded (ETL) from source to target systems, within data management platforms. Data Dictionaries are frequently created to collate all available metadata for a data set, including data models, data definitions, data lineage, business rules, etc.. Ensuring data quality Data is accurate per definition Using knowledge about data and associated processes to ascertain that the data as recorded matches the data as defined. Data matches original data source Data differences can be explained Profiling source and target data sets to determine equivalency, in light of the transformation and other business rules that take place between the two. Matching transformations between data sets to fully explain the differences between them. Data quality checklist and validation testing Data quality assessment Organizing and storing data during a research project

5 Design and create research problemspecific data models Analyzing requirements and other stakeholder input towards the specification of high-level and detailed information requirements (e.g., data model diagram, data dicitonaries, business rules, and high-level data lineage incuding sources and sourcing gap analysis). Design and create a research data mart Analyzing requirements and other stakeholder input towards the design of data structures to meet those specifications. Create a specific data extract from institutional data assets Extract-Transfer-Load (ETL) design and development Clinical Research data capture and integration (e.g. REDCAP, OpenClinica) Automated delivery (Informatica, Business Objects) Translate requirements for data and information into the correct conditions for extracting from institutional data assets, and executing these extractions with appropriate testing. Determine the algorithms by which data is taken from source system(s), transformed according to specific requirements, and loaded into target system(s). This includes registry creation, when appropriate. Capabilities for data processing and presentation on a scheduled basis, with the assistance of specified technological solutions. Interpreting, analyzing, publishing data Reporting and dashboarding Machine learning Big data computing The Data presentation layer, generally, here; composing extracts, then organizing and otherwise visaully aligning them to support user requirements (e.g., decision-making). The utilization of artificial intelligence systems that automatically learn and improve from experience without being explicitly programmed. Hadoop and Spark platforms for Big Data analytics using Map Reduce, In-Memory or other methods with special infrastructure.

6 High-performance computing Large scale batch and interactive computation supporting a handful to thousands of processors, massive system memory or Input/Output throughput greater than the workstation or departmental server. Statistical analysis Analyze data utilizing appropriate statistical techniques. Data visualization education Offering workshops, in-course instruction, in-person consultations; providing software and hardware. Sharing data Globus MiShare A world-wide system and software for transferring, sharing, and publishing data across all platforms and scales (laptop to super computer center). Secure File Transfer System, approved by the Michigan Medicine Compliance office for the transfer of files containing PHI, protected research data, or other sensitive information. Data security Security and risk analysis Reviewing data management architectures to ensure the appropriate protections are enforced across a solution. Archiving and preserving research data

7 Deep Blue data A repository offered by the University of Michigan Library that provides access and preservation services for digital research data that were developed or used in the support of research activities at U-M. Note that personally identifiable information, and any sensitive data under the purview of U-M Research Compliance Programs, cannot be accepted at this time. Regulations and compliance Planning for IRB review Planning for storage compliance Planning for HIPAA compliance Compliance with funder data sharing mandates, and public access policies for research publications