Research Data Management Training Research Articles

Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort.

Research data management has become an indispensable skill in modern neuroscience. Researchers can benefit from following good practices as well as from having proficiency in using particular software solutions. But as these domain-agnostic skills are commonly not included in domain-specific graduate education, community efforts increasingly provide early career scientists with opportunities for organised training and materials for self-study. Investing effort in user documentation and interacting with the user base can, in turn, help developers improve quality of their software. In this work, we detail and evaluate our multi-modal teaching approach to research data management in the DataLad ecosystem, both in general and with concrete software use. Spanning an online and printed handbook, a modular course suitable for in-person and virtual teaching, and a flexible collection of research data management tips in a knowledge base, our free and open source collection of training material has made research data management and software training available to various different stakeholders over the past five years.

A research data management (RDM) community for ELIXIR

Research data management (RDM) is central to the implementation of the FAIR (Findable Accessible, Interoperable, Reusable) and Open Science principles. Recognising the importance of RDM, ELIXIR Platforms and Nodes have invested in RDM and launched various projects and initiatives to ensure good data management practices for scientific excellence. These projects have resulted in a rich set of tools and resources highly valuable for FAIR data management. However, these resources remain scattered across projects and ELIXIR structures, making their dissemination and application challenging. Therefore, it becomes imminent to coordinate these efforts for sustainable and harmonised RDM practices with dedicated forums for RDM professionals to exchange knowledge and share resources. The proposed ELIXIR RDM Community will bring together RDM experts to develop ELIXIR’s vision and coordinate its activities, taking advantage of the available assets. It aims to coordinate RDM best practices and illustrate how to use the existing ELIXIR RDM services. The Community will be built around three integral pillars, namely, a network of RDM professionals, RDM knowledge management and RDM training expertise and resources. It will also engage with external stakeholders to leverage benefits and provide a forum to RDM professionals for regular knowledge exchange, capacity building and development of harmonised RDM practices, keeping in line with the overall scope of the RDM Community. In the short term, the Community aims to build upon the existing resources and ensure that the content of these remain up to date and fit for purpose. In the long run, the Community will aim to strengthen the skills and knowledge of its RDM professionals to support the emerging needs of the scientific community. The Community will also devise an effective strategy to engage with other ELIXIR structures and international stakeholders to influence and align with developments and solutions in the RDM field.

Evaluating an Instructional Intervention for Research Data Management Training

Objective – At a large research university in Canada, a research data management (RDM) specialist and two liaison librarians partnered to evaluate the effectiveness of an active learning component of their newly developed RDM training program. This empirical study aims to contribute a statistical analysis to evaluate an RDM instructional intervention. Methods – This study relies on a pre- and post-test quasi-experimental intervention during introductory RDM workshops offered 12 times between February 2022 and January 2023. The intervention consists of instruction on best practices related to file-naming conventions. We developed a grading rubric differentiating levels of proficiency in naming a file according to a convention reflecting RDM best practices and international standards. We used manual content analysis to independently code each pre- and post-instruction file name according to the rubric. Results – Comparing the overall average scores for each participant pre- and post-instruction intervention, we find that workshop participants, in general, improved in proficiency. The results of a Wilcoxon signed-rank test demonstrate that the difference between the pre- and post-test observations is statistically significant with a high effect size. In addition, a comparison of changes in pre- and post-test scores for each rubric element showed that participants grasped specific elements more easily (i.e., implementing an international standard for a date format) than others (i.e., applying information related to sequential versioning of files). Conclusion – The results of this study indicate that developing short and targeted interventions in the context of RDM training is worthwhile. In addition, the findings demonstrate how quantitative evaluations of instructional interventions can pinpoint specific topics or activities requiring improvement or further investigation. Overall, RDM learning outcomes grounded in practical competencies may be achieved through applied exercises that demonstrate immediate improvement directly to participants.

Building Research Data Management (RDM) Expertise and Training Resources in ELIXIR Nodes

Research data Management (RDM) is central to the implementation of the FAIR (Findable Accessible, Interoperable, Reusable) and Open Science principles. Recognising the importance of RDM, the ELIXIR-CONVERGE project was launched in 2020 and included a work package on Training & Capacity Building in Data Stewardship, in which 21 Nodes participate. Since there is a clear need for dissemination of RDM know-how, practices and resources and a demand for RDM training material and courses, for researchers, trainers and RDM professionals, the aim of the work package was to build a comprehensive, interconnected and sustainable RDM Training Portfolio across all Nodes, and to increase the RDM training expertise and capacity in ELIXIR Nodes. 
 RDM professionals, training experts and trainers from ELIXIR and beyond identified gaps in the Nodes’ RDM training programmes and defined priority topics (DMP, Data Stewardship, FAIR/metadata and Reproducibility) (Cardona et al, 2021) for training development. Collaborative work from all the ELIXIR Nodes during ELIXIR-CONVERGE resulted in an extensive Data Management/Data Stewardship (DM/DS) course portfolio (Cardona et al, 2021) which aligns with the Nodes’ RDM training strategies. The portfolio includes both generic DM/DS resources and materials, as well as specific materials related to the priority topics mentioned above, and it also includes training materials for specific domains (e.g. the Plant Demonstrator). The course portfolio is available in TeSS, in which training materials are tagged with RDM terms to increase their findability, and links to relevant training materials were included in RDMkit. In collaboration with the ELIXIR Training Platform, the learning paths methodology has been applied to RDM for data stewards and researchers (e.g. with the Plants Sciences and the System Biology Communities), and work is ongoing to extend the ELIXIR-GOBLET Train-the-Trainer programme to tackle the challenges that exist in teaching RDM. Moreover, during ELIXIR-CONVERGE, a strong RDM trainers network was formed, establishing solid RDM training expertise within the Nodes. This emerging network raised great interest in the Nodes, showing their willingness to work together on developing training materials, sharing their knowledge, and establishing best practices and standards for RDM training.
 The main RDM training related actions in ELIXIR going forward are: (i) gathering the RDM courses in the portfolio into learning paths for specific audiences, thus shaping the ELIXIR RDM curriculum, (ii) increasing findability and reusability of the developed courses and modules by optimising annotation and linking in TeSS, RDMkit and FAIR Cookbook, (iii) encouraging (re)use of the RDM materials in ELIXIR Nodes, and (iv) increasing RDM training capacity in Nodes by finalising and launching the RDM Train the Trainer programme.In 2023, the ELIXIR RDM Community was initiated, and the network of RDM trainers will be an integral part of this Community. This will enable the upskilling of RDM trainers and scaling up RDM training capacity in the Nodes, while bridging to RDM professionals outside ELIXIR as well. 

A pilot study on social science graduate students' data core competency

In the field of research, teaching graduate students how to manage data on a regular basis is becoming increasingly crucial. However, there is a lack of adequate evidence in the literature to determine the necessity of Research Data Management (RDM) training from the perspectives of social science student researchers. To address this gap, a pilot study was conducted to develop a data core competency measure to gauge the perceived importance of data competency among social science graduate students and assess their data core competency. Forty graduate students in a college of education at an US research university filled out a researcher-designed data core competency survey. This pilot study found that graduate students in the social sciences do not generally appreciate RDM. Consequently, the results underscore the need to foster an environment in graduate social science programs that value RDM best practices. The study suggested that future studies build on the preliminary findings by developing standardized data core competency measures.

Research data management needs assessment for social sciences graduate students: A mixed methods study.

The complexity and privacy issues inherent in social science research data makes research data management (RDM) an essential skill for future researchers. Data management training has not fully addressed the needs of graduate students in the social sciences. To address this gap, this study used a mixed methods design to investigate the RDM awareness, preparation, confidence, and challenges of social science graduate students. A survey measuring RDM preparedness and training needs was completed by 98 graduate students in a school of education at a research university in the southern United States. Then, interviews exploring data awareness, knowledge of RDM, and challenges related to RDM were conducted with 10 randomly selected graduate students. All participants had low confidence in using RDM, but United States citizens had higher confidence than international graduate students. Most participants were not aware of on-campus RDM services, and were not familiar with data repositories or data sharing. Training needs identified for social science graduate students included support with data documentation and organization when collaborating, using naming procedures to track versions, data analysis using open access software, and data preservation and security. These findings are significant in highlighting the topics to cover in RDM training for social science graduate students. Additionally, RDM confidence and preparation differ between populations so being aware of the backgrounds of students taking the training will be essential for designing student-centered instruction.

Multi-Stakeholder Research Data Management Training as a Tool to Improve the Quality, Integrity, Reliability and Reproducibility of Research

To ensure the quality and integrity of data and the reliability of research, data must be well documented, organised, and described. This calls for research data management (RDM) education for researchers. In light of 3 ECTS Basics of Research Data Management (BRDM) courses held between 2019 and 2021, we aim to find how a generic level multi-stakeholder training can improve STEM and HSS disciplines’ doctoral students’ and postdoc researchers’ competencies in RDM. The study uses quantitative, descriptive and inferential statistics to analyse respondents’ self-ratings of their competencies, and a qualitative grounded theory-inspired approach to code and analyse course participants’ feedback. Results: On average, based on the post-course surveys, respondents’ (n = 123) competencies improved one point on a four-level scale, from “little competence” (2) to “somewhat competent” (3). Participants also reported that the training would change their current practices in planning research projects, data management and documentation, acknowledging legal and data privacy viewpoints, and data collecting and organising. Participants indicated that it would be helpful to see legal and data privacy principles and regulations presented as concrete instructions, cases, and examples. The most requested continuing education topics were metadata and description, discipline specific cultures, and backup, version management, and storage. Conclusions: Regarding to the widely used criteria for successful training containing 1) active participation during training; 2) demand for RDM training; 3) increased participants’ knowledge and understanding of RDM and confidence in enacting RDM practices; and 4) positive post-training feedback, BRDM meets the criteria. This study shows that although reaching excellent competence in a RDM basics training is improbable, participants become aware of RDM and its contents and gain the elementary tools and basic skills to begin applying sound RDM practices in their research. Furthermore, participants are introduced to the academic and research support professionals and vice versa: Stakeholders will get to know the challenges that young researchers and research students encounter when applying RDM. The study reveals valuable information on doctoral students’ and postdoc researchers’ competencies, the impact of education on competencies, and further learning needs in RDM.

Research Data Management Training in Academic Libraries: A Scoping Review

Purpose: The present scoping review examines research data management (RDM) best practices and empirical studies in academic libraries between 2010 and 2021. Method: The current study developed systematic database searches to locate potential articles for inclusion and designed a detailed and systematic coding scheme to examine the substantive features of RDM and characteristics of RDM practice, with an emphasis on RDM instruction. Results and Discussion: The results from the current study demonstrated that there is great demand for RDM training after 2011. Furthermore, research about RDM training spread across North America, Europe, Asia Pacific, and elsewhere. The findings also proved that RDM training is essential for both STEM and non- STEM subjects but simultaneously indicated that non-STEM subjects such as the social sciences in particular lack RDM training. Results from the current literature also found that a large number of RDM training programs focused on the introduction of RDM or an RDM overview, without in-depth and discipline-based curriculum for researchers across domains. Additionally, this study identified a lack of quantitative research, especially statistical analysis, on the effect of RDM interventions. Conclusion: This study contributes to our comprehensive understanding of some essential elements associated with RDM training, with the primary finding that future practitioners in the RDM field would benefit from stronger collaboration with faculty or researchers to develop more discipline-based curriculums for RDM and more application-based approaches for teaching RDM.

Slow improvement to the archiving quality of open datasets shared by researchers in ecology and evolution.

Many leading journals in ecology and evolution now mandate open data upon publication. Yet, there is very little oversight to ensure the completeness and reusability of archived datasets, and we currently have a poor understanding of the factors associated with high-quality data sharing. We assessed 362 open datasets linked to first- or senior-authored papers published by 100 principal investigators (PIs) in the fields of ecology and evolution over a period of 7 years to identify predictors of data completeness and reusability (data archiving quality). Datasets scored low on these metrics: 56.4% were complete and 45.9% were reusable. Data reusability, but not completeness, was slightly higher for more recently archived datasets and PIs with less seniority. Journal open data policy, PI gender and PI corresponding author status were unrelated to data archiving quality. However, PI identity explained a large proportion of the variance in data completeness (27.8%) and reusability (22.0%), indicating consistent inter-individual differences in data sharing practices by PIs across time and contexts. Several PIs consistently shared data of either high or low archiving quality, but most PIs were inconsistent in how well they shared. One explanation for the high intra-individual variation we observed is that PIs often conduct research through students and postdoctoral researchers, who may be responsible for the data collection, curation and archiving. Levels of data literacy vary among trainees and PIs may not regularly perform quality control over archived files. Our findings suggest that research data management training and culture within a PI's group are likely to be more important determinants of data archiving quality than other factors such as a journal's open data policy. Greater incentives and training for individual researchers at all career stages could improve data sharing practices and enhance data transparency and reusability.

Characterizing the research data management practices of NIH biomedical researchers indicates the need for better support at laboratory level.

The study investigated the research data management (RDM) practices of biomedical researchers at the National Institutes of Health (NIH) representing various biomedical disciplines. This study aimed to analyse the state of biomedical researchers' RDM practices based on RDM practice levels (individual, laboratory, institution and external). The findings of the study are expected to provide directions to information professionals for effective RDM services. Semi-structured interviews with 11 researchers were conducted. The interviews were analysed by levels of RDM practices. The findings revealed that biomedical researchers focus on storing and sharing data and that RDM is performed mainly at the individual level. There seems to be a lack of laboratory level RDM system that allows consistent RDM practices among researchers. External RDM practice is often challenged by not having one responsible for RDM. Findings suggested a need for an agreed RDM system and customized support, particularly at the laboratory level. Also, institutional support can help researchers prepare for long term data preservation. Our suggestions emphasize the importance of RDM training and support for long term data preservation, especially at the laboratory level.

Data Services Librarians’ Responsibilities and Perspectives on Research Data Management

This study of data services librarians is part of a series of studies examining the current roles and perspectives on Research Data Management (RDM) services in higher education. Reviewing current best practices provides insights into the role-based responsibilities for RDM services that data services librarians perform, as well as ways to improve and create new services to meet the needs of their respective university communities. Objectives: The objectives of this article are to provide the context of research data services through a review of past studies, explain how they informed this qualitative study, and provide the methods and results of the current study. This study provides an in-depth overview of the overall job responsibilities of data services librarians and as well as their perspectives on RDM through job analyses. Methods: Job analysis interviews provide insight and context to the tasks employees do as described in their own words. Interviews with 10 data services librarians recruited from the top 10 public and top 10 private universities according to the 2020 Best National University Rankings in the US News and World Reports were asked 30 questions concerning their overall job tasks and perspectives on RDM. Five public and five private data services librarians were interviewed. The interviews were recorded and transcribed. The transcriptions were analyzed in NVivo using a grounded theory application of open, axial, and selective coding to generate categories and broad themes based on the responses using synonymous meanings. Results: The results presented here provide the typical job tasks of data services librarians that include locating secondary data, reviewing data management plans (DMPs), conducting outreach, collaborating, and offering RDM training. Fewer data services librarians assisted with data curation or manage an institutional repository. Discussion: The results indicate that there may be different types of data services librarians depending on the mix of responsibilities. Academic librarianship will benefit from further delineation of job titles using tasks while planning, advertising, hiring, and evaluating workers in this emerging area. There remain many other explorations needed to understand the challenges and opportunities for data services librarians related to RDM. Conclusions: This article concludes with a proposed matrix of job tasks that indicates different types of data services librarians to inform further study. Future job descriptions, training, and education will all benefit from differentiating between the many associated research data services roles and with increased focus on research data greater specializations will emerge.

An initial evaluation of research data management online training at the University of New South Wales

In response to low research data management engagement at the University of New South Wales, Australia, an introductory research data management online training was developed and rolled out to all newly enrolled Higher Degree Research candidates. This article outlines the development process of the research data management online training and provides an initial evaluation of the training from the perspectives of the candidates and the university. As such, this article joins up with existing literature on research data management training to assist institutions and research data management stakeholders with the development of research data management training to help researchers and research students enact research data management best practices. Overall, the majority of the candidates ( n = 643) were satisfied with the quality of the training and found it helpful. The benefits the training brought to the university are mapped out by linking institutional research data management problems, the research data management online training’s design and findings into a coherent narrative.

A Usability Evaluation of Research Data Management Librarian Academy (RDMLA): Examining the Impact of Learner Differences in Pedagogical Usability

This paper reports the results of a usability and user experience (UX) investigation into the Research Data Management Librarian Academy (RDMLA) course site, a free online learning platform developed through community effort. Remote synchronous usability test sessions were run with 42 participants. Participants completed a pre-session interview, performed tasks related to the course orientation unit and two content units, and filled out a post-session questionnaire. Significant differences were found among people with different demographic attributes, educational backgrounds, occupations, and levels of research data management (RDM) training, in areas related to self-rating, task performance, and post-task and post-session evaluations of RDMLA. Significant correlations occurred among participants’ pre-session, post-task, and post-session ratings, as well as performance measures. Qualitative descriptions of the participants’ perceptions of the future of RDM in LIS were also reported. The study results indicate a need to add a new criterion - diverse learner population - into the framework of pedagogical usability.

Delineating the successful features of research data management training: a systematic review

ABSTRACT Research Data Management (RDM) is an inherently complex area that presents a challenge for institutions to effectively upskill people in RDM and get them to enact RDM best practices. This systematic review aims to provide a reference point for institutions and academic developers to design and deliver effective training for their students and staff to navigate the RDM landscape. After narrowing down from a pool of 1256 articles across 7 databases to 28 articles for analysis, this systematic review provides an overview of available RDM training and identifies three main themes in relation to key features of this RDM training.

Shifting Horizons: A Literature Review of Research Data Management Train-the-Trainer Models for Library and Campus-Wide Research Support Staff in Canadian Institutions

Objective – In consideration of emerging national Research Data Management (RDM) policy and infrastructure, this literature review seeks answers to the following questions: 1) What is the most effective way for a Canadian research university to build capacity among library and campus-wide research support staff, with a view towards providing coordinated RDM support services for our researcher community?2) What international training models and course offerings are available and appropriate for a local context?3) What national guidelines and best practices for pedagogical design and delivery can be adapted for a local context? Methods – This literature review synthesizes a total of 13 sources: 9 articles, 2 book chapters, and 2 whitepapers. The whitepapers were selected for a narrative literature review because of their focus on case studies detailing train-the-trainer models. Within the 13 sources we found 14 key case studies. This review serves as a supplement to the 2017 CARL Portage Training Expert Group white paper, “Research Data Management Training Landscape in Canada,” the focus of which was to identify RDM training gaps in order to recommend a coordinated approach to RDM training in a national environment. Results – The narrative review of case studies revealed three thematic areas. Firstly, pedagogical challenges were identified, including the need to target training to RDM support staff such as librarians and researchers, as they comprise distinct groups of trainees with divergent disciplinary vocabularies and incentives for training. Secondly, the case studies cover a broad range of pedagogical models including single or multiple sessions, self-directed or instructor-led, in-person or online instruction, and a hybrid of the two. Finally, RDM training also emerged as a key factor in community building within library staff units, among service units on campus, and with campus research communities. Conclusion – RDM training programs at local institutions should be guided by a set of principles aligned with the training methods, modes of assessment, and infrastructure development timeline outlined in a national training strategy. When adapting principles and training strategies to a local context, the following trends in the literature should be considered: librarians and researchers must have meaningful incentives to undertake training in RDM or to join a community of practice; disciplinary-specific instruction is preferable to general instruction; a librarian’s own training opportunities will influence their ability to provide discipline-specific RDM instruction to researchers; in-person training opportunities improve learning retention and produce beneficial secondary effects, whereas online instruction is most effective when paired with an in-person component; generalized third-party RDM training should be adapted to local context to be meaningful. Future directions for RDM training will integrate into open access and digital scholarship training, and into cross-disciplinary, open science communities of practice.

Scientists’ Research Data Management Questions: Lessons Learned at a Data Help Desk

This study investigates scientists’ data needs to provide a basis for professionals in research data management (RDM) to tailor services to meet those needs. Eighty-one participants completed a survey after they had asked a question at a Data Help Desk staffed by data management professionals at one of two science conferences. The qualitative responses were coded for common themes, and the quantitative questions were tabulated to compare results between the two conferences. The combined results provide an overview of scientists’ questions and training experiences on RDM. The study found that 70 percent of scientists in the survey had no prior RDM training, and the most common RDM need for both ecologists and geologists was storage. This study provides evidence that scientists need additional information about RDM. Traditional service desk models offer one way of assisting them with data needs, but the results indicate a need for more training to meet the expectations of data sharing. Academic and other libraries should consider expanding hours and services for RDM to provide help where and when scientists need it.

Piloting a Safe Health Researcher course

Robust and standardised licensing and governance frameworks are used to ensure that datasets intended for research use are made available under the terms and conditions specified by a data owner.
 The UK Data Service makes use of the Five Safes framework to operate its 3-tier data access policy, and ensuring that data classified as personal data can be made available via appropriate legal gateways. This set of principles has gained traction with national statistics around the world, yet it is remarkably absent in the narrative of data access for health research. The health domain tends to focus on ‘data sharing agreements’, and less on training around trust, security and disclosure. The concept of a Safe Health Researcher is missing, yet is appealing.
 The UK Data Service has been piloting such a half day course, with colleagues in the health domain. The training helps consolidate the less well defined idea of a ‘bona fide’ researcher, typically required by funders such as the UK’s Medical Research Council when accessing their data assets. The term bonafide makes some assumptions about the credentials of the researchers, yet fails to ‘test’ them, instead relying on ‘trust ‘underwritten by the individual’s university, and maybe a short online security course.
 While purposeful breaches are certainly not common place, a researcher accessing personal or sensitive data would benefit from a structured half-day course that covers aspects of: potential/ actual disclosure risk in health data and appropriate access pathways; safeguards to be put in place when data with risk are shared; and what might constitute a published ‘unsafe’ output, i.e. with a risk of disclosure. The training focuses on health data and research examples, and draws on aspects of the research data management and publishing training undertaken by the UK Data Service (e.g. Corti et al, 2014) and on the UK Statistics Authority approved ‘Safe Research Training’ (SRT) course, which leads to the Accredited Researcher status.

Education Needs in Research Data Management for Science-Based Disciplines

Research data management is a prominent and evolving consideration for the academic community, especially in scientific disciplines. This research study surveyed 131 graduate students and 79 faculty members in the sciences at two public doctoral universities to determine the importance, knowledge, and interest levels around research data management training and education. The authors adapted 12 competencies for measurement in the study. Graduate students and faculty ranked the following areas most important among the 12 competencies: ethics and attribution, data visualization, and quality assurance. Graduate students indicated they were least knowledgeable and skilled in data curation and re-use, metadata and data description, data conversion and interoperability, and data preservation. Their responses generally matched the perceptions of faculty. The study also examined how graduate students learn research data management, and how faculty perceive that their students learn research data management. Results showed that graduate students utilize self-learning most often and that faculty may be less influential in research data management education than they perceive. Responses for graduate students between the two institutions were not statistically different, except in the area of perceived deficiencies in data visualization competency.

“How Do I Do That?” A Literature Review of Research Data Management Skill Gaps of Canadian Health Sciences Information Professionals

Abstract: There is a recognized need to provide research data management (RDM) services in health sciences libraries. A review of the literature reveals numerous strategies to provide training for health sciences librarians as they provide RDM services to health sciences researchers, faculty, and students. However, no consensus emerges through this literature review with respect to RDM training initiatives. With training initiatives being developed and documented, more in-depth research will emerge that verifies which initiatives have the greatest success for upskilling information professionals in managing research data. This is an area where future library and information studies research can be conducted. It is the hope that with this literature review, I can conduct my own survey to gain more perspective on RDM in a Canadian health sciences library context.

Adapting data management education to support clinical research projects in an academic medical center.

BackgroundLibrarians and researchers alike have long identified research data management (RDM) training as a need in biomedical research. Despite the wealth of libraries offering RDM education to their communities, clinical research is an area that has not been targeted. Clinical RDM (CRDM) is seen by its community as an essential part of the research process where established guidelines exist, yet educational initiatives in this area are unknown.Case PresentationLeveraging my academic library’s experience supporting CRDM through informationist grants and REDCap training in our medical center, I developed a 1.5 hour CRDM workshop. This workshop was designed to use established CRDM guidelines in clinical research and address common questions asked by our community through the library’s existing data support program. The workshop was offered to the entire medical center 4 times between November 2017 and July 2018. This case study describes the development, implementation, and evaluation of this workshop.ConclusionsThe 4 workshops were well attended and well received by the medical center community, with 99% stating that they would recommend the class to others and 98% stating that they would use what they learned in their work. Attendees also articulated how they would implement the main competencies they learned from the workshop into their work. For the library, the effort to support CRDM has led to the coordination of a larger institutional collaborative training series to educate researchers on best practices with data, as well as the formation of institution-wide policy groups to address researcher challenges with CRDM, data transfer, and data sharing.