Publications

2020
Alexander SM, Jones K, Bennett NJ, Budden A, Cox M, Crosas M, Game ET, Geary J, Hardy RD, Johnson JT, et al. Qualitative data sharing and synthesis for sustainability science. Nature Sustainability [Internet]. 2020;3 :81–88. Publisher's VersionAbstract
Socio–environmental synthesis as a research approach contributes to broader sustainability policy and practice by reusing data from disparate disciplines in innovative ways. Synthesizing diverse data sources and types of evidence can help to better conceptualize, investigate and address increasingly complex socio–environmental problems. However, sharing qualitative data for re-use remains uncommon when compared to sharing quantitative data. We argue that qualitative data present untapped opportunities for sustainability science, and discuss practical pathways to facilitate and realize the benefits from sharing and reusing qualitative data. However, these opportunities and benefits are also hindered by practical, ethical and epistemological challenges. To address these challenges and accelerate qualitative data sharing, we outline enabling conditions and suggest actions for researchers, institutions, funders, data repository managers and publishers.
Trisovic A, Durbin P, Schlatter T, Durand G, Barbosa S, Brooke D, Crosas M. Advancing Computational Reproducibility in the Dataverse Data Repository Platform, in P-RECS '20: Proceedings of the 3rd International Workshop on Practical Reproducible Evaluation of Computer Systems. ; 2020 :15–20. Publisher's VersionAbstract
Recent reproducibility case studies have raised concerns showing that much of the deposited research has not been reproducible. One of their conclusions was that the way data repositories store research data and code cannot fully facilitate reproducibility due to the absence of a runtime environment needed for the code execution. New specialized reproducibility tools provide cloud-based computational environments for code encapsulation, thus enabling research portability and reproducibility. However, they do not often enable research discoverability, standardized data citation, or long-term archival like data repositories do. This paper addresses the shortcomings of data repositories and reproducibility tools and how they could be overcome to improve the current lack of computational reproducibility in published and archived research outputs.
2019
Fenner M, Crosas M, Grethe JS, Kennedy D, Hermjakob H, Rocca-Serra P, Durand G, Berjon R, Karcher S, Martone M, et al. A data citation roadmap for scholarly data repositories. Scientific Data [Internet]. 2019;6 (28). Publisher's VersionAbstract
This article presents a practical roadmap for scholarly data repositories to implement data citation in accordance with the Joint Declaration of Data Citation Principles, a synopsis and harmonization of the recommendations of major science policy bodies. The roadmap was developed by the Repositories Expert Group, as part of the Data Citation Implementation Pilot (DCIP) project, an initiative of FORCE11.org and the NIH-funded BioCADDIE (https://biocaddie.org) project. The roadmap makes 11 specific recommendations, grouped into three phases of implementation: a) required steps needed to support the Joint Declaration of Data Citation Principles, b) recommended steps that facilitate article/data publication workflows, and c) optional steps that further improve data citation support provided by data repositories. We describe the early adoption of these recommendations 18 months after they have first been published, looking specifically at implementations of machine-readable metadata on dataset landing pages.
Wilkinson MD, Dumontier M, Sansone S-A, da Santos LOBS, Prieto M, Batista D, McQuilton P, Kuhn T, Rocca-Serra P, Crosas M, et al. Evaluating FAIR maturity through a scalable, automated, community-governed framework. Scientific Data [Internet]. 2019;6 (174). Publisher's VersionAbstract
Transparent evaluations of FAIRness are increasingly required by a wide range of stakeholders, from scientists to publishers, funding agencies and policy makers. We propose a scalable, automatable framework to evaluate digital resources that encompasses measurable indicators, open source tools, and participation guidelines, which come together to accommodate domain relevant community-defined FAIR assessments. The components of the framework are: (1) Maturity Indicators – community-authored specifications that delimit a specific automatically-measurable FAIR behavior; (2) Compliance Tests – small Web apps that test digital resources against individual Maturity Indicators; and (3) the Evaluator, a Web application that registers, assembles, and applies community-relevant sets of Compliance Tests against a digital resource, and provides a detailed report about what a machine “sees” when it visits that resource. We discuss the technical and social considerations of FAIR assessments, and how this translates to our community-driven infrastructure. We then illustrate how the output of the Evaluator tool can serve as a roadmap to assist data stewards to incrementally and realistically improve the FAIRness of their resources.
2018
Crosas M, Gautier J, Karcher S, Kirilova D, Otalora G, Schwartz A. Data policies of highly-ranked social science journals. SocArXiv. 2018.Abstract

By encouraging and requiring that authors share their data in order to publish articles, scholarly journals have become an important actor in the movement to improve the openness of data and the reproducibility of research. But how many social science journals encourage or mandate that authors share the data supporting their research findings? How does the share of journal data policies vary by discipline? What influences these journals’ decisions to adopt such policies and instructions? And what do those policies and instructions look like?

We discuss the results of our analysis of the instructions and policies of 291 highly-ranked journals publishing social science research, where we studied the contents of journal data policies and instructions across 14 variables, such as when and how authors are asked to share their data, and what role journal ranking and age play in the existence and quality of data policies and instructions. We also compare our results to the results of other studies that have analyzed the policies of social science journals, although differences in the journals chosen and how each study defines what constitutes a data policy limit this comparison.

We conclude that a little more than half of the journals in our study have data policies. Agreater share of the economics journals have data policies and mandate sharing, followed by political science/international relations and psychology journals.

Finally, we use our findings to make several recommendations: Policies should include the terms “data,” “dataset” or more specific terms that make it clear what to make available; policies should include the benefits of data sharing; journals, publishers, and associations need to collaborate more to clarify data policies; and policies should explicitly ask for qualitative data.

This paper has won the IASSIST & Carto 2018 Best Paper award.

data_policies_of_highly-ranked_social_science_journals.pdf
2017
Crosas M. Cloud Dataverse: A Data Repository Platform for the Cloud. CIO Review [Internet]. 2017. Publisher's Version
Pasquier T, Lau MK, Trisovic A, Boose ER, Couturier B, Crosas M, Ellison AM, Gibson V, Jones CR, Seltzer M. If these data could talk. Scientific Data [Internet]. 2017;4 (170114). Publisher's VersionAbstract
In the last few decades, data-driven methods have come to dominate many fields of scientific inquiry. Open data and open-source software have enabled the rapid implementation of novel methods to manage and analyze the growing flood of data. However, it has become apparent that many scientific fields exhibit distressingly low rates of reproducibility. Although there are many dimensions to this issue, we believe that there is a lack of formalism used when describing end-to-end published results, from the data source to the analysis to the final published results. Even when authors do their best to make their research and data accessible, this lack of formalism reduces the clarity and efficiency of reporting, which contributes to issues of reproducibility. Data provenance aids both reproducibility through systematic and formal records of the relationships among data sources, processes, datasets, publications and researchers.
2016
McKinney B, Meyer PA, Crosas M, Sliz P. Extension of research data repository system to support direct compute access to biomedical datasets: enhancing Dataverse to support large datasets. Ann. N.Y. Acad. Sci. [Internet]. 2016;1387 :95-104. Publisher's VersionAbstract
Access to experimental X‐ray diffraction image data is important for validation and reproduction of macromolecular models and indispensable for the development of structural biology processing methods. In response to the evolving needs of the structural biology community, we recently established a diffraction data publication system, the Structural Biology Data Grid (SBDG, data.sbgrid.org), to preserve primary experimental datasets supporting scientific publications. All datasets published through the SBDG are freely available to the research community under a public domain dedication license, with metadata compliant with the DataCite Schema (schema.datacite.org). A proof‐of‐concept study demonstrated community interest and utility. Publication of large datasets is a challenge shared by several fields, and the SBDG has begun collaborating with the Institute for Quantitative Social Science at Harvard University to extend the Dataverse (dataverse.org) open‐source data repository system to structural biology datasets. Several extensions are necessary to support the size and metadata requirements for structural biology datasets. In this paper, we describe one such extension—functionality supporting preservation of file system structure within Dataverse—which is essential for both in‐place computation and supporting non‐HTTP data transfers.
Meyer PA, et al. Data publication with the structural biology data grid supports live analysis. Nature Communications. 2016;(10882).Abstract

Access to experimental X-ray diffraction image data is fundamental for validation and reproduction of macromolecular models and indispensable for development of structural biology processing methods. Here, we established a diffraction data publication and dissemination system, Structural Biology Data Grid (SBDG; data.sbgrid.org), to preserve primary experimental data sets that support scientific publications. Data sets are accessible to researchers through a community driven data grid, which facilitates global data access. Our analysis of a pilot collection of crystallographic data sets demonstrates that the information archived by SBDG is sufficient to reprocess data to statistics that meet or exceed the quality of the original published structures. SBDG has extended its services to the entire community and is used to develop support for other types of biomedical data sets. It is anticipated that access to the experimental data sets will enhance the paradigm shift in the community towards a much more dynamic body of continuously improving data analysis.

Bar-Sinai M, Sweeney L, Crosas M. DataTags, Data Handling Policy Spaces and the Tags Language, in In Proceedings of the International Workshop on Privacy Engineering, IEEE. IEEE ; 2016. Publisher's VersionAbstract
Widespread sharing of scientific datasets holds great promise for new scientific discoveries and great risks for personal privacy. Dataset handling policies play the critical role of balancing privacy risks and scientific value. We propose an extensible, formal, theoretical model for dataset handling policies. We define binary operators for policy composition and for comparing policy strictness, such that propositions like "this policy is stricter than that policy" can be formally phrased. Using this model, The policies are described in a machine-executable and human-readable way. We further present the Tags programming language and toolset, created especially for working with the proposed model. Tags allows composing interactive, friendly questionnaires which, when given a dataset, can suggest a data handling policy that follows legal and technical guidelines. Currently, creating such a policy is a manual process requiring access to legal and technical experts, which are not always available. We present some of Tags' tools, such as interview systems, visualizers, development environment, and questionnaire inspectors. Finally, we discuss methodologies for questionnaire development. Data for this paper include a questionnaire for suggesting a HIPAA compliant data handling policy, and formal description of the set of data tags proposed by the authors in a recent paper.
Wilkinson MD, Dumontier M, Aalbersberg IJJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten J-W, da Santos LBS, Bourne PE, et al. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data. 2016;160018.Abstract

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

2015
Starr J, Castro E, Crosas M, Dumontier M, Downs RR, Duerr R, Haak LL, Haendel M, Herman I, Hodson S, et al. Achieving human and machine accessibility of cited data in scholarly publications. PeerJ Computer Science [Internet]. 2015. Publisher's VersionAbstract

Reproducibility and reusability of research results is an important concern in scientific communication and science policy. A foundational element of reproducibility and reusability is the open and persistently available presentation of research data. However, many common approaches for primary data publication in use today do not achieve sufficient long-term robustness, openness, accessibility or uniformity. Nor do they permit comprehensive exploitation by modern Web technologies. This has led to several authoritative studies recommending uniform direct citation of data archived in persistent repositories. Data are to be considered as first-class scholarly objects, and treated similarly in many ways to cited and archived scientific and scholarly literature. Here we briefly review the most current and widely agreed set of principle-based recommendations for scholarly data citation, the Joint Declaration of Data Citation Principles (JDDCP). We then present a framework for operationalizing the JDDCP; and a set of initial recommendations on identifier schemes, identifier resolution behavior, required metadata elements, and best practices for realizing programmatic machine actionability of cited data. The main target audience for the common implementation guidelines in this article consists of publishers, scholarly organizations, and persistent data repositories, including technical staff members in these organizations. But ordinary researchers can also benefit from these recommendations. The guidance provided here is intended to help achieve widespread, uniform human and machine accessibility of deposited data, in support of significantly improved verification, validation, reproducibility and re-use of scholarly/scientific data.

Crosas M, King G, Honaker J, Sweeney L. Automating Open Science for Big Data. ANNALS of the American Academy of Political and Social Science. 2015;659 (1) :260-273.Abstract

The vast majority of social science research uses small (megabyte- or gigabyte-scale) datasets. these fixed- scale datasets are commonly downloaded to the researcher’s computer where the analysis is performed. the data can be shared, archived, and cited with well-established technologies, such as the Dataverse Project, to support the published results. the trend toward big data—including large-scale streaming data—is starting to transform research and has the potential to impact policymaking as well as our understanding of the social, economic, and political problems that affect human societies. However, big data research poses new challenges to the execution of the analysis, archiving and reuse of the data, and reproduction of the results. Downloading these datasets to a researcher’s computer is impractical, leading to analyses taking place in the cloud, and requiring unusual expertise, collaboration, and tool development. the increased amount of information in these large datasets is an advantage, but at the same time it poses an increased risk of revealing personally identifiable sensitive information. In this article, we discuss solutions to these new challenges so that the social sciences can realize the potential of big data.

Altman M, Borgman C, Crosas M, Martone M. An Introduction to the Joint Principles of Data Citation. Bulletin of the Association for Information Science and Technology. 2015;41 (3) :43-45.Abstract

Data citation is rapidly emerging as a key practice supporting data access, sharing and reuse, as well as sound and reproducible scholarship. Consensus data citation principles, articulated through the Joint Declaration of Data Citation Principles, represent an advance in the state of the practice and a new consensus on citation.

Altman M, Castro E, Crosas M, Durbin P, Garnett A, Whitney J. Open Journal Systems and Dataverse Integration-- Helping Journals to Upgrade Data Publication for Reusable Research. Code4Lib Journal. 2015;(30).Abstract

This article describes the novel open source tools for open data publication in open access journal workflows. This comprises a plugin for Open Journal Systems that supports a data submission, citation, review, and publication workflow; and an extension to the Dataverse system that provides a standard deposit API. We describe the function and design of these tools, provide examples of their use, and summarize their initial reception. We conclude by discussing future plans and potential impact.

Sweeney L, Crosas M. An Open Science Platform for the Next Generation of Data. Arxiv.org Computer Science, Computers and Scoiety [Internet]. 2015. Publisher's VersionAbstract

Imagine an online work environment where researchers have direct and immediate access to myriad data sources and tools and data management resources, useful throughout the research lifecycle. This is our vision for the next generation of the Dataverse Network: an Open Science Platform (OSP). For the first time, researchers would be able to seamlessly access and create primary and derived data from a variety of sources: prior research results, public data sets, harvested online data, physical instruments, private data collections, and even data from other standalone repositories. Researchers could recruit research participants and conduct research directly on the OSP, if desired, using readily available tools. Researchers could create private or shared workspaces to house data, access tools, and computation and could publish data directly on the platform or publish elsewhere with persistent, data citations on the OSP. This manuscript describes the details of an Open Science Platform and its construction. Having an Open Science Platform will especially impact the rate of new scientific discoveries and make scientific findings more credible and accountable. (This manuscript was originally conceived in 2013)

Sweeney L, Crosas M, Bar-Sinai M. Sharing Sensitive Data with Confidence: the DataTags System. Technology Science. 2015.Abstract

Society generates data on a scale previously unimagined. Wide sharing of these data promises to improve personal health, lower healthcare costs, and provide a better quality of life. There is a tendency to want to share data freely. However, these same data often include sensitive information about people that could cause serious harms if shared widely. A multitude of regulations, laws and best practices protect data that contain sensitive personal information. Government agencies, research labs, and corporations that share data, as well as review boards and privacy officers making data sharing decisions, are vigilant but uncertain. This uncertainty creates a tendency not to share data at all. Some data are more harmful than other data; sharing should not be an all-or-nothing choice. How do we share data in ways that ensure access is commensurate with risks of harm?

Bar-Sinai M. Big Data Technology Literature Review. 2015.
Altman M, Castro E, Crosas M, Durbin P, Garnett A, Whitney J. Open Journal Systems and Dataverse Integration– Helping Journals to Upgrade Data Publication for Reusable Research. The Code4Lib Journal [Internet]. 2015;30. Publisher's VersionAbstract
This article describes the novel open source tools for open data publication in open access journal workflows. This comprises a plugin for Open Journal Systems that supports a data submission, citation, review, and publication workflow; and an extension to the Dataverse system that provides a standard deposit API. We describe the function and design of these tools, provide examples of their use, and summarize their initial reception. We conclude by discussing future plans and potential impact.
Quigley E. Usability Testing Driven Redesign of Dataverse, an Open Source Data Repository. University of Massachusetts and New England Area Librarian e-Science Symposium [Internet]. 2015. Publisher's Version

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