@article {673605,
	title = {A large-scale study on research code quality and execution},
	journal = {Arxiv},
	year = {2021},
	abstract = {This article presents a study on the quality and execution of research code from publicly-available replication datasets at the Harvard Dataverse repository. Research code is typically created by a group of scientists and published together with academic papers to facilitate research transparency and reproducibility. For this study, we define ten questions to address aspects impacting research reproducibility and reuse. First, we retrieve and analyze more than 2000 replication datasets with over 9000 unique R files published from 2010 to 2020. Second, we execute the code in a clean runtime environment to assess its ease of reuse. Common coding errors were identified, and some of them were solved with automatic code cleaning to aid code execution. We find that 74\% of R files crashed in the initial execution, while 56\% crashed when code cleaning was applied, showing that many errors can be prevented with good coding practices. We also analyze the replication datasets from journals{\textquoteright} collections and discuss the impact of the journal policy strictness on the code re-execution rate. Finally, based on our results, we propose a set of recommendations for code dissemination aimed at researchers, journals, and repositories.},
	url = {https://arxiv.org/abs/2103.12793},
	author = {Trisovic, Ana and Lau, Matthew K. and Pasquier, Thomas and Crosas, Merc{\`e}}
}
@article {671149,
	title = {Repository Approaches to Improving the Quality of Shared Data and Code},
	journal = {MDPI Data},
	year = {2021},
	abstract = {Sharing data and code for reuse has become increasingly important in scientific work over the past decade. However, in practice, shared data and code may be unusable, or published results obtained from them may be irreproducible. Data repository features and services contribute significantly to the quality, longevity, and reusability of datasets. This paper presents a combination of original and secondary data analysis studies focusing on computational reproducibility, data curation, and gamified design elements that can be employed to indicate and improve the quality of shared data and code. The findings of these studies are sorted into three approaches that can be valuable to data repositories, archives, and other research dissemination platforms.},
	url = {https://www.mdpi.com/2306-5729/6/2/15},
	author = {Trisovic, Ana and Katherine Mika and Ceilyn Boyd and Sebastian Feger and Crosas, Merc{\`e}}
}
@article {650772,
	title = {Aggregated mobility data could help fight COVID-19},
	journal = {Science},
	volume = {23 March},
	number = {Letters},
	year = {2020},
	url = {https://science.sciencemag.org/content/early/2020/03/20/science.abb8021},
	author = {Buckee, Caroline and Balsari, Satchit and Chan, Jennifer and Crosas, Merc{\`e} and Francesca Dominici and Urs Gasser and Grad, Yonatan and Grenfell, Bryan and Halloran, M. Elizabeth and Kraemer, Moritz and Marc Lipsitch and Metcalf, Jessica and Lauren Ancel Meyers and Perkins, Alex and Santillana, Mauricio and Scarpino, Samuel and Cecile Viboud and Amy Wesolowski and Schroeder, Andrew}
}
@article {650769,
	title = {Qualitative data sharing and synthesis for sustainability science},
	journal = {Nature Sustainability},
	number = {3},
	year = {2020},
	pages = {81-88},
	abstract = {Socio{\textendash}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{\textendash}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.},
	url = {https://www.nature.com/articles/s41893-019-0434-8},
	author = {Alexander, Steven and Jones, Kristal and Bennet, Nathan and Buden, Amber and Cox, Michael and Crosas, Merc{\`e} and Game, Edward and Geary, Janis and Hardy, Dean and Johnson, Jay and Karcher, Sebastian and Motzer, Nicole and Pittman, Jeremy and Randell, Heather and Silva, Julie and Pinto da Silva, Patricia and Strasser, Carly and Strawhacker, Colleen and Stuhl, Andrew and Weber, Nic}
}
@article {666985,
	title = {FAIR principles: Interpretations and implementation considerations},
	journal = {Data Intelligence},
	volume = {2},
	number = {1-2},
	year = {2020},
	pages = {10-29},
	abstract = {The FAIR principles have been widely cited, endorsed and adopted by a broad range of stakeholders since their publication in 2016. By intention, the 15 FAIR guiding principles do not dictate specific technological implementations, but provide\&nbsp;guidance\&nbsp;for improving Findability, Accessibility, Interoperability and Reusability of digital resources. This has likely contributed to the broad adoption of the FAIR principles, because individual stakeholder communities can implement their own FAIR solutions. However, it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible implementations. Thus, while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways, for true interoperability we need to support convergence in implementation choices that are widely accessible and (re)-usable. We introduce the concept of\&nbsp;FAIR implementation considerations\&nbsp;to assist accelerated global participation and convergence towards accessible, robust, widespread and consistent FAIR implementations. Any self-identified stakeholder community may either\&nbsp;choose\&nbsp;to reuse solutions from existing implementations, or when they spot a gap, accept the\&nbsp;challenge\&nbsp;to create the needed solution, which, ideally, can be used again by other communities in the future. Here, we provide interpretations and implementation considerations (choices and challenges) for each FAIR principle.},
	url = {https://www.mitpressjournals.org/doi/full/10.1162/dint_r_00024},
	author = {Annika Jacobsen and Ricardo de Miranda Azevedo and Nick Juty and Dominique Batista and Simon Coles and Ronald Cornet and M{\'e}lanie Courtot and Crosas, Merc{\`e} and Dumontier, Michel and et al}
}
@booklet {674173,
	title = {Whitepaper on Creating a Platform for the Sharing of Sensitive Online Data},
	year = {2019},
	abstract = {Dataverse, DataTags, and a Decade Building a Widely-Used Data Repository Platform},
	url = {https://securelysharingdata.com/whitepapers.html},
	author = {Crosas, Merc{\`e}}
}
@article {645899,
	title = {Evaluating FAIR maturity through a scalable, automated, community-governed framework},
	journal = {Nature-Springer Scientific Data},
	volume = {6},
	number = {174},
	year = {2019},
	abstract = {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 {\textendash} community-authored specifications that delimit a specific automatically-measurable FAIR behavior; (2) Compliance Tests {\textendash} 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 {\textquotedblleft}sees{\textquotedblright} 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.},
	url = {https://www.nature.com/articles/s41597-019-0184-5},
	author = {Mark D. Wilkinson and Dumontier, Michel and Susanna-Asunta Sansone and Luiz Olavo and Mario Prieto and Dominique Batista and Peter McQuilton and Tobias Kuhn and Rocca-Serra, Philippe and Mercѐ Crosas and Erik Shultes}
}
@article {635438,
	title = {A Data Citation Roadmap for Scholarly Data Repositories},
	journal = {Nature-Springer Scientific Data},
	volume = {6},
	number = {28},
	year = {2019},
	abstract = {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.},
	url = {https://www.nature.com/articles/s41597-019-0031-8},
	author = {Martin Fenner and Crosas, Merc{\`e} and Jeffrey Grethe and Kennedy, David and Henning Hermjakob and Phillippe Rocca-Serra and Gustavo Durand and Robin Berjon and Karcher, Sebastian and Maryann Martone and Tim Clark}
}
@article {611124,
	title = {Data policies of highly-ranked social science journals},
	journal = {SocArXiv},
	volume = {March},
	year = {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{\textquoteright} 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. A greater 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 {\textquotedblleft}data,{\textquotedblright} {\textquotedblleft}dataset{\textquotedblright} 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 \&amp; Carto 2018 Best Paper award.

},
	author = {Crosas, Merc{\`e} and Julian Gautier and Karcher, Sebastian and Dessi Kirilova and Gerard Otalora and Abigail Schwartz}
}
@article {609923,
	title = {Sharing and Preserving Computational Analyses for Posterity with Encapsulator.},
	journal = {Computing in Science and Engineering (CiSE), IEEE},
	volume = {May},
	year = {2018},
	abstract = {Open data and open-source software may be part of the solution to sciences reproducibility crisis, but they are insufficient to guarantee reproducibility. Requiring minimal end-user expertise, encapsulator creates a {\textquotedblleft}time capsule{\textquotedblright} with reproducible code in a self-contained computational environment. encapsulator provides end-users with a fully-featured desktop environment for reproducible research.\&nbsp;},
	url = {https://arxiv.org/pdf/1803.05808.pdf},
	author = {Pasquier, Thomas and Lau, Matthew K. and Xueyuan Han and Elizabeth Fong and Barbara S. Lerner and Emery Boose and Crosas, Merc{\`e} and Aaron Ellison and Margo Seltzer}
}
@article {583141,
	title = {If These Data Could Talk},
	journal = {Nature Scientific Data},
	year = {2017},
	abstract = {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\&nbsp;systematic\&nbsp;and\&nbsp;formal\&nbsp;records of the relationships among data sources, processes, datasets, publications and researchers.},
	url = {https://www.nature.com/articles/sdata2017114},
	author = {Pasquier, Thomas and Matthew Lau and Trisovic, Ana and Emery Boose and Ben Couturierer and Crosas, Merc{\`e} and Aaron Ellison and Gibson, Valerie and Chris Jones and Margo Seltzer}
}
@magazinearticle {529471,
	title = {Cloud Dataverse: A Data Repository Platform for the Cloud},
	journal = {CIO Review},
	year = {2017},
	url = {http://openstack.cioreview.com/cxoinsight/cloud-dataverse-a-data-repository-platform-for-the-cloud-nid-24199-cid-120.html},
	author = {Crosas, Merc{\`e}}
}
@article {doi:10.1056/NEJMsb1616595,
	title = {Data Authorship as an Incentive to Data Sharing},
	journal = {New England Journal of Medicine},
	volume = {Sounding Board},
	number = {March 29, 2017},
	year = {2017},
	month = {29 March, 2017},
	doi = {10.1056/NEJMsb1616595},
	url = {http://dx.doi.org/10.1056/NEJMsb1616595},
	author = {Bierer, Barbara E. and Crosas, Merc{\`e} and Pierce, Heather H.}
}
@article {497951,
	title = {A Data Citation Roadmap for Scholarly Data Repositories},
	journal = {BioArxiv [preprint]},
	year = {2016},
	month = {28 December 2016},
	abstract = {
	This article presents a practical roadmap for scholarly data repositories to implement data citation in accordance with the Joint Declaration of Data Citation Principles (Data Citation Synthesis Group, 2014), a synopsis and harmonization of the recommendations of major science policy bodies. The roadmap was developed by the Repositories Early Adopters Expert Group, part of the Data Citation Implementation Pilot (DCIP) project (FORCE11, 2015), an initiative of FORCE11.org and the NIH BioCADDIE (2016) program. 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.

},
	url = {https://www.biorxiv.org/content/early/2016/12/28/097196},
	author = {Martin Fenner and Merce Crosas and Jeffrey Grethe and Kennedy, David and Henning Hermjakob and Phillipe Roca-Serra and Robin Berjon and Maryann Martone and Timothy Clark}
}
@article {475551,
	title = {Extension of research data repository system to support direct compute access to biomedical datasets: enhancing Dataverse to support large datasets},
	journal = {The Annals of the New York Academy of Sciences},
	year = {2016},
	month = {10 November 2016},
	abstract = {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\&nbsp;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{\textemdash}functionality supporting preservation of file system structure within Dataverse{\textemdash}which is essential for both in-place computation and supporting non-HTTP data transfers.},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/nyas.13272/full},
	author = {Bill McKinney and Pete Meyer and Crosas, Merc{\`e} and Piotr Sliz}
}
@article {380646,
	title = {Data publication with the structural biology data grid supports live analysis},
	journal = {Nature Communications},
	number = {10882},
	year = {2016},
	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.},
	url = {http://www.nature.com/ncomms/2016/160307/ncomms10882/full/ncomms10882.html},
	author = {Meyer, P. and et al.}
}
@article {380661,
	title = {DataTags, Data Handling Policy Spaces and the Tags Language},
	journal = {2016 IEEE Security and Privacy Workshops (SPW)},
	year = {2016},
	abstract = {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{\textquoteright} 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.},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7527746},
	author = {Michael Bar-Sinai and Latanya Sweeney and Crosas, Merc{\`e}}
}
@article {372751,
	title = {The FAIR Guiding Principles for scientific data management and stewardship},
	journal = {Nature Scientific Data},
	number = {160018},
	year = {2016},
	abstract = {There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders{\textemdash}representing academia, industry, funding agencies, and scholarly publishers{\textemdash}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.},
	url = {http://www.nature.com/articles/sdata201618},
	author = {Mark Wilkinson and et al}
}
@article {388616,
	title = {An Introduction to the Joint Principles of Data Citation},
	journal = {Bulletin of the Association for Information Science and Technology},
	volume = {41},
	number = {3},
	year = {2015},
	pages = {43-44},
	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.},
	url = {http://www.asis.org/Bulletin/Feb-15/FebMar15_RDAP_Altman_EtAl.html},
	author = {Altman, M. and Borgman, C and Crosas, M and Martone, M}
}
@article {299446,
	title = {Open Journal Systems and Dataverse Integration-- Helping Journals to Upgrade Data Publication for Reusable Research},
	journal = {Code4Lib Journal},
	number = {Issue 30},
	year = {2015},
	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.},
	url = {http://journal.code4lib.org/articles/10989},
	author = {Altman, M. and Castro, E. and Crosas, M and Durbin, P. and Garnett, A. and Whitney, J.}
}
@article {299451,
	title = {Sharing Sensitive Data with Confidence: the DataTags System},
	journal = {Technology Science},
	year = {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?},
	url = {http://techscience.org/a/2015101601/},
	author = {Sweeney, L. and Crosas, M and Bar-Sinai, M.}
}
@workingpaper {271766,
	title = {An Open Science Platform for the Next Generation of Data},
	journal = {Arxiv.org Computer Science, Computers and Scoiety},
	year = {2015},
	abstract = {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)},
	url = {http://arxiv.org/abs/1506.05632},
	author = {Latanya Sweeney and Merce Crosas}
}
@article {starr2015achieving,
	title = {Achieving human and machine accessibility of cited data in scholarly publications},
	journal = {PeerJ Computer Science},
	year = {2015},
	publisher = {PeerJ PrePrints},
	url = {https://peerj.com/articles/cs-1/},
	author = {Starr, Joan and Castro, Eleni and Crosas, Merc{\`e} and Dumontier, Michel and Downs, Robert R and Duerr, Ruth and Haak, Laurel and Haendel, Melissa and Herman, Ivan and Hodson, Simon and others}
}
@article {crosas2014automating,
	title = {Automating Open Science for Big Data},
	journal = {ANNALS of the American Academy of Political and Social Science},
	volume = {659},
	number = {1},
	year = {2015},
	pages = {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{\textquoteright}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{\textemdash}including large-scale streaming data{\textemdash}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{\textquoteright}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.},
	url = {http://ann.sagepub.com/content/659/1/260.abstract},
	author = {Merce Crosas and James Honaker and Gary King and Latanya Sweeney}
}
@article {pepe2014astronomers,
	title = {How Do Astronomers Share Data? Reliability and Persistence of Datasets Linked in AAS Publications and a Qualitative Study of Data Practices among US Astronomers},
	journal = {PLoS ONE},
	volume = {9},
	year = {2014},
	publisher = {Public Library of Science},
	author = {Pepe, Alberto and Goodman, Alyssa and Muench, August and Merce Crosas and Erdmann, Christopher}
}
@article {goodman2014ten,
	title = {Ten simple rules for the care and feeding of scientific data},
	journal = {PLoS computational biology},
	volume = {10},
	year = {2014},
	publisher = {Public Library of Science},
	author = {Goodman, Alyssa and Pepe, Alberto and Blocker, Alexander W. and Borgman, Christine L. and Cranmer, Kyle and Merce Crosas and Di Stefano, Rosanne and Gil, Yolanda and Groth, Paul and Hedstrom, Margaret and Hogg, David and Kashyap, Vinay and Mahabal, Ashish and Siemiginowska, Aneta and Slavkovic, Aleksandra}
}
@article {crosas2013data,
	title = {A data sharing story},
	journal = {Journal of eScience Librarianship},
	volume = {1},
	year = {2013},
	pages = {7},
	url = {http://escholarship.umassmed.edu/jeslib/vol1/iss3/7/},
	author = {Merce Crosas}
}
@article {altman2013evolution,
	title = {The evolution of data citation: From principles to implementation},
	journal = {IASSIST Quarterly},
	volume = {37},
	year = {2013},
	url = {http://www.iassistdata.org/iq/evolution-data-citation-principles-implementation},
	author = {Micah Altman and Crosas, Merc{\`e}}
}
@conference {rajasekar2013sociometric,
	title = {Sociometric Methods for Relevancy Analysis of Long Tail Science Data},
	booktitle = {Social Computing (SocialCom), 2013 International Conference on},
	year = {2013},
	pages = {1{\textendash}6},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Rajasekar, Arcot and Sankaran, Sharlini and Lander, Howard and Carsey, Tom and Jonathan Crabtree and Crosas, M and G. King and Kum, Hye-Chung and Zhan, Justin}
}
@article {crosas2011dataverse,
	title = {The dataverse network{\textregistered}: an open-source application for sharing, discovering and preserving data},
	journal = {D-lib Magazine},
	volume = {17},
	year = {2011},
	pages = {2},
	publisher = {Corporation for National Research Initiatives},
	url = {http://www.dlib.org/dlib/january11/crosas/01crosas.html},
	author = {Merce Crosas}
}
@article {knapp2000atomic,
	title = {Atomic carbon in the envelopes of carbon-rich post-asymptotic giant branch stars},
	journal = {The Astrophysical Journal},
	volume = {534},
	year = {2000},
	pages = {324},
	publisher = {IOP Publishing},
	author = {Knapp, G. R. and Crosas, M and Young, K and Ivezi{\'c}, {\.Z}eljko}
}
@article {knapp1999circumstellar,
	title = {The Circumstellar Envelope of pi Gru},
	journal = {arXiv preprint astro-ph/9903338},
	year = {1999},
	author = {Knapp, G. R. and Young, K and Crosas, M}
}
@article {sakamoto1999counterrotating,
	title = {Counterrotating nuclear disks in Arp 220},
	journal = {The Astrophysical Journal},
	volume = {514},
	year = {1999},
	pages = {68},
	publisher = {IOP Publishing},
	author = {Sakamoto, K and Scoville, NZ and Yun, Min S and Crosas, M and Genzel, R and Tacconi, LJ}
}
@article {wood1999gg,
	title = {GG Tauri{\textquoteright}s Circumbinary Disk: Models for Near-Infrared Scattered-Light Images and 13CO (J= 1{\textrightarrow} 0) Line Profiles},
	journal = {The Astrophysical Journal},
	volume = {516},
	year = {1999},
	pages = {335},
	publisher = {IOP Publishing},
	author = {Wood, Kenneth and Merce Crosas and Ghez, Andrea}
}
@article {knapp1999light,
	title = {The light curve and evolutionary status of the carbon star V Hya},
	journal = {arXiv preprint astro-ph/9907234},
	year = {1999},
	author = {Knapp, G. R. and Dobrovolsky, SI and Ivezic, Z. and Young, K and Crosas, M and Mattei, JA and Rupen, MP}
}
@inbook {crosas1998radiative,
	title = {Radiative Transfer in a Turbulent Expanding Molecular Envelope: Application to Mira},
	booktitle = {Dust and Molecules in Evolved Stars},
	year = {1998},
	pages = {189{\textendash}192},
	publisher = {Springer},
	organization = {Springer},
	author = {Crosas, Merc{\`e} and Menten, Karl M and Young, Ken and Phillips, Thomas G}
}
@article {crosas1997physical,
	title = {Physical parameters of the IRC+ 10216 circumstellar envelope: new constraints from submillimeter observations},
	journal = {The Astrophysical Journal},
	volume = {483},
	year = {1997},
	pages = {913},
	publisher = {IOP Publishing},
	author = {Crosas, Merc{\`e} and Menten, Karl M}
}
@article {crosas1996spallation,
	title = {Spallation in Active Galactic Nuclei},
	journal = {The Astrophysical Journal},
	volume = {465},
	year = {1996},
	pages = {659},
	author = {Merce Crosas and Weisheit, Jon}
}
@article {crosas1993cosmic,
	title = {Cosmic Rays in AGNs},
	journal = {Revista Mexicana de Astronomia y Astrofisica},
	volume = {27},
	year = {1993},
	pages = {107},
	author = {Crosas, M and Weisheit, J}
}
@article {crosas1993hydrogen,
	title = {Hydrogen molecules in quasar broad-line regions},
	journal = {Monthly Notices of the Royal Astronomical Society},
	volume = {262},
	year = {1993},
	pages = {359{\textendash}368},
	publisher = {Oxford University Press},
	author = {Crosas, Merc{\`e} and Weisheit, Jon C}
}
