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Against the Grain Manuscript 8419 The Opportunities and Challenges of Research Data and Software for Libraries and Institutional Repositories Tom Morrell Follow this and additional works at: https://docs.lib.purdue.edu/atg  Part of the Library and Information Science Commons This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact epubs@purdue.edu for additional information. 30 Against the Grain / November 2019 <http://www.against-the-grain.com>continued on page 31The Opportunities and Challenges of Research Data and Software for Libraries and Institutional Repositoriesby Tom Morrell  (Research Data Specialist, Caltech Library)  <tmorrell@caltech.edu>Long-term availability of data and software generated by researchers is a massive challenge.  For libraries, this challenge is also an opportunity to leverage relationships with researchers and utilize ex-pertise in creating metadata and making content available over the long term.  At Caltech, a strong research data repository was created by keeping the services simple and providing researchers an easy-to-use platform to share data and software (data.caltech.edu).  In just two years of operation, CaltechDATA has received an impressive number of submis-sions from over 1% of campus researchers in a wide variety of disciplines.  The repository has already powered a discipline-specific data resource, custom visualizations, and allowed for rapid development of many new features.The publication and data management practices currently used by the research com-munity are clearly insufficient to maximize the value of research, resulting in inaccessible data, non-reproducible data, and worst of all lost data.  Even simple measurements that can easily be stored in a text file, such as the length of a bird beak, present significant challenges for data reproducibility and accessibility.  A study looking at 20 years of biological organism measurement data found that on average only 20% percent of data files were available when requested, and availability decreased over time (Vines et al., 2014).  Even when data are received, they may not be correct or usable by other researchers.  In this case, only 13% of papers had data that could be used to reproduce the analysis from the original work (Andrew et al., 2015).  This example shows some of the current challenges for accessing and using research data.  Larger and more complex types of data and software will prove even more difficult to preserve and reuse.  Depositing data files and software in a repository is the solution to data availability challenges.  There are thousands of disci-pline-specific data repositories that have been developed to store and improve the reusability of research outputs for a specific communities (Pampel et al., 2013).  Successful efforts, such as the Protein Data Bank (PDB) for protein structural data, GenBank for genomics data, and WormBase for nematode model organ-ism data, have made large amounts of data available in a standardized way (Benson et al., 2013; Berman, Kleywegt, Nakamura, & Markley, 2014; Lee et al., 2018).  However, two major challenges for discipline-specific repositories are scope and funding.  The scope of disciplinary-specific repositories is unlikely to be sufficient to meet researcher needs.  For example, Caltech researchers publish ap-proximately 4,000 peer-reviewed publications annually, and most rely on significant amounts of data and software.  Much of this innovative work is interdisciplinary and simply does not fit into existing disciplinary repositories.  If a field is still developing, it is nearly impossible to standardize file formats and experiment-spe-cific metadata.  Discipline-specific repositories are also often funded through competitive short-term research grants, making long-term funding challenging.  For each new grant a jus-tification must be made as to how funding will have a major new impact on the research com-munity.  It can be difficult to get funding for the ongoing and unexciting work of maintaining access to data (Van Horn & Gazzaniga, 2013).The development and promotion of the FAIR (Findable, Accessible, Interoperable, and Reproducible) principles for research data has provided a broader understanding of the requirements for effective data sharing (Wilkinson et al., 2016).  Key components of making data FAIR include assigning appropri-ate metadata, using persistent identifiers, and providing human and machine access to the data.  Efforts are underway to enable research-ers to make their data FAIR, and publishers are determining how to include data and software citation as part of publications (Cousijn et al., 2018; McQuilton et al., 2016).  The COPDESS “Commitment Statement in the Earth, Space, and Environmental Sciences for Depositing and Sharing Data” statement makes recommen-dations to ensure open data, including having journals stop accepting data files and software as supplemental files and directing researchers to put data and software in appropriate repos-itories (COPDESS, 2019).  This commitment statement is a major step in the right direction and has received signatures from most major publishers.  While the current statement is solely for geoscience-related data, the quick adoption by publishers suggests it may easily translate to other fields (Stall et al., 2019).  The transition to FAIR data in repositories will result in many questions from researchers, especially since a significant amount of data currently stored in supplemental information or on personal computers will need to find a home. One approach to the challenge of increas-ing data deposit demands is to simplify the problem.  In the current era of limitless and cost-effective cloud storage, the annual price for storing 100 TB of data in geographically-re-dundant cloud storage is less than the average cost of a single chemistry journal subscription (Romaine, 2019).  Open source repository software like Invenio and Dataverse provides community supported ways of managing data. All institutions have access to the technology to store and make files persist over time, but there are two challenging requirements for a successful repository: collecting files and software from researchers and ensuring that content remains available over the long term. Libraries are uniquely positioned to tackle these challenges since they are experts in stor-ing, archiving and describing materials.  They have existing relationships with researchers and deep experience with metadata.  Libraries also have a history of preserving content and making thoughtful decisions about retention. Existing institutional funding models for libraries solve the major challenge of long-term sustainability common for disciplinary repositories.  Similarly, researchers are likely to be more willing to store their data locally at their own institution.  Under the auspices of a university library, all data and software at an institution can be captured by the institution and paid for by the institution. Despite many advantages, insti-tutional data storage at a library has traditionally limited the amount of customization available to researchers to support discipline-specific require-ments.  However, modern repository platforms with persistent identifiers and APIs can balance standardization and customization.  Persistent identifiers such as DOIs easily provide federated metadata for discovery and APIs allow access to the underlying data files for customized devel-opment.  This allows a disciplinary or project repository to easily build custom features on top of data that is stored in the institutional repository.  For example, the Total Carbon Column Observing Network (TCCON) has their data service (tccondata.org) built on the Caltech institutional data repository, CaltechDATA (data.caltech.edu).  TCCON maintains their own data processing pipeline and website.  They have complete control of how their data files are organized, can embed custom metadata within their files, and can provide private access to data to members of the consortium.  However, the public access to files is via DOIs that resolve to CaltechDATA landing pages.  At the end of the TCCON data processing pipeline, files are transferred to CaltechDATA automatically using an API. The data files in CaltechDATA can also be accessed programmatically using an API or included in custom visualizations and 31Against the Grain / November 2019 <http://www.against-the-grain.com>   other processing pipelines.  Even if TCCON ceases operation and the tccondata.org site goes offline, all the important data files will still be accessible via CaltechDATA and the Caltech Library.  Another challenge for institutional repos-itories run by libraries is a limited history of receiving large volumes of data submissions. A 2017 survey from ACRL found that most library institutional repositories receive one or fewer datasets per month (Hudson-Vitale et al., 2017).  Library-managed repositories will need to be more efficient in order to tackle the vol-ume of data anticipated from new researchers being required to provide FAIR data to support publications.  CaltechDATA has been in oper-ation since summer 2017 and in two years of operation has received over 1,000 records from more than 1% of campus researchers (including faculty, staff, postdocs, and graduate students) from a broad range of disciplines.  A one-page deposit form was developed for CaltechDATA that is straightforward, quick, and makes it easy for all authenticated campus researchers to sub-mit files with metadata based on the DataCite schema.  The deposit form shows only the most critical fields to the researcher by default, but a complete set of metadata is made available if they want to build a more complex record. Upon submission, the user immediately gets a DOI that can be included in a publication. All metadata is transmitted to DataCite for aggregation to encourage dataset discovery. Users can also automatically submit software via the CaltechDATA GitHub integration and generate and update records using the Caltech-DATA API.  Data deposits are encouraged by not having a library approval step, so the submission process can easily scale to thou-sands of records per year.  Although the library does not manually curate record metadata and files, the quality has been remarkably high as the researchers feel responsible for their own CaltechDATA records.  Since CaltechDATA records are public, the quality of the submitted data and metadata directly affect the public image of the researcher, encouraging high quality submissions. Even though the underlying CaltechDATA repository is simple, Caltech Library has been able to quickly build new features such as automated metadata updates and visualizations to support researchers and data users.  When a dataset in CaltechDATA is cited by a new publication, the publication is automatically linked in the CaltechDATA item’s metadata using CrossRef Event Data.  The researcher who submitted the CaltechDATA record can also choose to receive an email notification every time their item is cited.  Similarly, when a dataset is referenced by a completed thesis in the CaltechTHESIS repository, the thesis is au-tomatically linked in the CaltechDATA item’s metadata.  Project-specific visualizations, such as our geology thesis map (maps.library.caltech.edu) were developed to show where data were collected over time and to promote Caltech research to the broader community. With the geology thesis map, a visitor views an image of the world and can zoom in to spe-cific sites and retrieve images of the original maps and illustrations generated by Caltech researchers since the 1920s.  This feature uses the read API to collect data from the repository. In support of software preservation, Caltech was an early adopter of the CodeMeta standard which allows researchers to provide more com-plete metadata as part of their code repository. For all software preserved in CaltechDATA the CodeMeta file can be extracted and used to update metadata in the record.  CaltechDATA also supports interactive software reuse using Binder, an open-source service that can be used to re-run data analysis in a Jupyter notebook or other programming environment from visitors’ web browser (Morrell, 2019).  These new features have been developed outside of the repository software stack, and can conceptually be applied to any API-enabled repository.  All these new features could be developed quickly as they don’t impact the basic functionality of the repository.By keeping things simple, library data services can be possible for all institutions. At Caltech, 1 FTE is dedicated to the data repository, with support from liaison librar-ians for outreach and submission support. The Opportunities and Challenges ...from page 30continued on page 3232 Against the Grain / November 2019 <http://www.against-the-grain.com>The existing relationships liaison librarians have with researchers is critical for making researchers aware of library data services and providing support for discipline-specific repositories, journal requirements, and meta-data creation.  The CaltechDATA repository is based on Invenio 3, which is an open-source repository system first developed at CERN. TIND, which provides commercial support for Invenio-based repositories, runs the hosted Invenio instance for Caltech.  For libraries that do not want to run their own repository, they can aid researchers submitting data or software to discipline-specific or available general re-positories such as Zenodo, Harvard Dataverse, or Dryad.  Many of these repositories provide APIs that can be used to automate submissions and access data for reuse.Libraries have a unique opportunity to provide solutions for the data and software preservation challenges that plague the sci-entific community.  Maintaining the record of scientific knowledge, which now includes data and software, requires institutional backing to succeed.  By developing simple repository services that are compliant with the FAIR prin-ciples, partnering with disciplinary repositories to act as storage agents, and working to meet the needs of researchers, libraries can ensure that research data and software remains open and available for years to come.bibliographyAndrew, R. L., Albert, A. Y. K., Renaut, S., Rennison, D. J., bock, D. g., and vines, T.  (2015).  Assessing the reproducibility of discriminant function analyses.  PeerJ.  https://doi.org/10.7717/peerj.1137The Opportunities and Challenges ...from page 31benson, D. A., Cavanaugh, M., Clark, K., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J., and Sayers, E. W.  (2013).  GenBank. Nucleic Acids Research, 41(Database issue), D36-42.  https://doi.org/10.1093/nar/gks1195berman, H. M., Kleywegt, g. J., Na-kamura, H., and Markley, J. L.  (2014). The Protein Data Bank archive as an open data resource.  J. Comput.  Aided. Mol. Des., 1009–1014.  https://doi.org/10.1007/s10822-014-9770-yCOPDESS.  (2019).  Commitment State-ment in the Earth, Space, and Environmental Sciences.  Retrieved July 2, 2019, from http://www.copdess.org/enabling-fair-data-project/commitment-to-enabling-fair-data-in-the-earth-space-and-environmental-sciences/Cousijn, H., Kenall, A., ganley, E., Har-rison, M., Kernohan, D., Lemberger, T., … Clark, T.  (2018).  A data citation roadmap for scientific publishers.  Scientific Data, 5, 180259–180259.  https://doi.org/10.1038/sdata.2018.259Hudson-vitale, C., Imker, H., Johnston, L. R., Carlson, J., Kozlowski, W., Olen-dorf, R., and Stewart, C.  (2017).  SPEC Kit 354 Data Curation.  Association of Research Libraries.Lee, R. Y. N., Howe, K. L., Harris, T. W., Arnaboldi, v., Cain, S., Chan, J., … Sternberg, P. W.  (2018).  WormBase 2017: Molting into a new stage.  Nucleic Acids Research, 46(D1), D869–D874.  https://doi.org/10.1093/nar/gkx998McQuilton, P., gonzalez-beltran, A., Rocca-Serra, P., Thurston, M., Lister, A., Maguire, E., and Sansone, S.-A.  (2016). BioSharing: Curated and crowd-sourced meta-data standards, databases and data policies in the life sciences.  Database, 2016.  https://doi.org/10.1093/database/baw075Morrell, T. E.  (2019).  caltechlibrary/caltechdata_usage: Jupyter notebook with visualization of submissions to CaltechDATA (Version v0.0.2).  https://doi.org/10.22002/d1.1250Pampel, H., vierkant, P., Scholze, F., bertelmann, R., Kindling, M., Klump, J., … Dierolf, U.  (2013).  Making research data repositories visible: The re3data.org registry. PLoS One, 8(11).  https://doi.org/10.1371/journal.pone.0078080Romaine, S. b., barbara Albee, and Sion.  (2019, April 4).  Deal or No Deal | Pe-riodicals Price Survey 2019.  Library Journal. Retrieved from https://www.libraryjournal.com?detailStory=Deal-or-No-Deal-Periodi-cals-Price-Survey-2019Stall, S., Yarmey, L., Cutcher-gershen-feld, J., Hanson, b., Lehnert, K., Nosek, b., … Wyborn, L.  (2019).  Make scientific data FAIR.  Nature, 570(7759), 27.  https://doi.org/10.1038/d41586-019-01720-7van Horn, J. D., and gazzaniga, M. S. (2013).  Why share data? Lessons learned from the fMRIDC.  NeuroImage, 82, 677–682.  https://doi.org/10.1016/j.neuroim-age.2012.11.010vines, T. H., Albert, A. Y. K., Andrew, R. L., Débarre, F., bock, D. g., Franklin, M. T., … Rennison, D. J.  (2014).  The availability of research data declines rapidly with article age.  Curr. Biol., 24(1), 94–97.  https://doi.org/10.1016/j.cub.2013.11.014Wilkinson, M. D., Dumontier, M., Aal-bersberg, Ij. J., Appleton, g., Axton, M., baak, A., … Mons, b.  (2016).  The FAIR Guiding Principles for scientific data manage-ment and stewardship.  Sci. Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18  continued on page 33Meeting Institutional Goals by Working Beyond Institutional Wallsby John Chodacki  (California Digital Library, University of California Office of the President)  <John.Chaodacki@ucop.edu>and Daniella Lowenberg  (California Digital Library, University of California Office of the President)   <Daniella.Lowenberg@ucop.edu>Researchers from across the University of California (UC) publish more than 50,000 articles annually.  Underlying most of these articles are datasets, many of which have not been published.  Even if these datasets were published, the UC system (like any university) does not have the ability to track or index them.  While the scale of the UC’s research outputs may not be typical of other universities, our story and approach to tackling these issues have been similar to those of other colleges and universities. In 2014, in an effort to address this problem, California Digital Library (CDL) set out to develop an easy submis-sion system on top of our digital preservation repository. That system was called Dash.  After receiving a Sloan Foundation grant to reimagine Dash as an open source, easy way to publish data, we worked to create an easy and user-friendly interface for UC researchers to publish and preserve their data at UC.  The goal was to get as many datasets (suitable for a general repository) as possible.  To attain this goal, our team spent years doing mass outreach to UC researchers, building out new features requested by these researchers, and trying to convince publishers and research workflow systems to integrate with Dash.The result?  Five hundred deposits over three years.We spent a significant amount of time with research-ers to make sure our decisions kept researchers in mind. Despite adopting this researcher-centric approach, we quickly recognized that the project presented several hur-dles to executing and building what researchers genuinely value (Narayan & Luca, 2017).  So, we realized we had to

The Opportunities and Challenges of Research Data and
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