Sorting Things Out: Classification and Its Consequences.

To thrive, the field that links biologists and their data urgently needs structure, recognition and support.

Big data: The future of biocuration.

We must all accept that science is data and that data are science, and thus provide for, and justify the need for the support of, much-improved data curation. (Hanson, Sugden, & Alberts) 
 
Researchers are producing an unprecedented deluge of data by using new methods and instrumentation. Others may wish to mine these data for new discoveries and innovations. However, research data are not readily available as sharing is common in only a few fields such as astronomy and genomics. Data sharing practices in other fields vary widely. Moreover, research data take many forms, are handled in many ways, using many approaches, and often are difficult to interpret once removed from their initial context. Data sharing is thus a conundrum. Four rationales for sharing data are examined, drawing examples from the sciences, social sciences, and humanities: (1) to reproduce or to verify research, (2) to make results of publicly funded research available to the public, (3) to enable others to ask new questions of extant data, and (4) to advance the state of research and innovation. These rationales differ by the arguments for sharing, by beneficiaries, and by the motivations and incentives of the many stakeholders involved. The challenges are to understand which data might be shared, by whom, with whom, under what conditions, why, and to what effects. Answers will inform data policy and practice. © 2012 Wiley Periodicals, Inc.

科研数据共享的挑战 (The Conundrum of Sharing Research Data)

We must all accept that science is data and that data are science, and thus provide for, and justify the need for the support of, much-improved data curation. (Hanson, Sugden, & Alberts)

Researchers are producing an unprecedented deluge of data by using new methods and instrumentation. Others may wish to mine these data for new discoveries and innovations. However, research data are not readily available as sharing is common in only a few fields such as astronomy and genomics. Data sharing practices in other fields vary widely. Moreover, research data take many forms, are handled in many ways, using many approaches, and often are difficult to interpret once removed from their initial context. Data sharing is thus a conundrum. Four rationales for sharing data are examined, drawing examples from the sciences, social sciences, and humanities: (1) to reproduce or to verify research, (2) to make results of publicly funded research available to the public, (3) to enable others to ask new questions of extant data, and (4) to advance the state of research and innovation. These rationales differ by the arguments for sharing, by beneficiaries, and by the motivations and incentives of the many stakeholders involved. The challenges are to understand which data might be shared, by whom, with whom, under what conditions, why, and to what effects. Answers will inform data policy and practice. © 2012 Wiley Periodicals, Inc.

The conundrum of sharing research data

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.

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ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

Results are presented from the Data Curation Profiles project research, on who is willing to share what data with whom and when. Emerging from scientists' discussions on sharing are several dimensions suggestive of the variation in both what it means 'to share' and how these processes are carried out. This research indicates that data curation services will need to accommodate a wide range of subdisciplinary data characteristics and sharing practices. As part of a larger set of strategies emerging across academic institutions, institutional repositories (IRs) will contribute to the stewardship and mobilization of scientific research data for e-Research and learning. There will be particular types of data that can be managed well in an IR context when characteristics and practices are well understood. Findings from this study elucidate scientists' views on 'sharable' forms of data-the particular representation that they view as most valued for reuse by others within their own research areas-and the anticipated duration for such reuse. Reported sharing incidents that provide insights into barriers to sharing and related concerns on data misuse are included.

Data sharing, small science and institutional repositories

The launch of the US BRAIN and European Human Brain Projects coincides with growing international efforts toward transparency and increased access to publicly funded research in the neurosciences. The need for data-sharing standards and neuroinformatics infrastructure is more pressing than ever. However, 'big science' efforts are not the only drivers of data-sharing needs, as neuroscientists across the full spectrum of research grapple with the overwhelming volume of data being generated daily and a scientific environment that is increasingly focused on collaboration. In this commentary, we consider the issue of sharing of the richly diverse and heterogeneous small data sets produced by individual neuroscientists, so-called long-tail data. We consider the utility of these data, the diversity of repositories and options available for sharing such data, and emerging best practices. We provide use cases in which aggregating and mining diverse long-tail data convert numerous small data sources into big data for improved knowledge about neuroscience-related disorders.

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Big data from small data: data-sharing in the 'long tail' of neuroscience

This paper presents a brief literature review and then introduces the methods, design, and construction of the Data Curation Profile, an instrument that can be used to provide detailed information on particular data forms that might be curated by an academic library. These data forms are presented in the context of the related sub-disciplinary research area, and they provide the flow of the research process from which these data are generated. The profiles also represent the needs for data curation from the perspective of the data producers, using their own language. As such, they support the exploration of data curation across different research domains in real and practical terms. With the sponsorship of the Institute of Museum and Library Services, investigators from Purdue University and the University of Illinois interviewed 19 faculty subjects to identify needs for discovery, access, preservation, and reuse of their research data. For each subject, a profile was constructed that includes information about his or her general research, data forms and stages, value of data, data ingest, intellectual property, organization and description of data, tools, interoperability, impact and prestige, data management, and preservation. Each profile also presents a specific dataset supplied by the subject to serve as a concrete example. The Data Curation Profiles are being published to a public wiki for questions and discussion, and a blank template will be disseminated with guidelines for others to create and share their own profiles. This study was conducted primarily from the viewpoint of librarians interacting with faculty researchers; however, it is expected that these findings will complement a wide variety of data curation research and practice outside of librarianship and the university environment.

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Constructing Data Curation Profiles

Scholarship and disciplinary practices

While problems related to the curation and preservation of scientific data are receiving considerable attention from the information science and digital repository communities, relatively little progress has been made on approaches for evaluating the value of data to inform investment in acquisition, curation, and preservation. Adapting Hjorland's concept of the “epistemological potential” of documents, we assert that analytic potential, or the value of data for analysis beyond its original use, should guide development of data collections for repositories aimed at supporting research. Three key aspects of the analytic potential of data are identified and discussed: preservation readiness, potential user communities, and fit for purpose. Based on evidence from research from the Data Conservancy initiative, we demonstrate how the analytic potential of data can be determined and applied to build large-scale data collections suited for grand challenge science.

Melissa H. Cragin

Papers

Data sharing, small science and institutional repositories

Big data from small data: data-sharing in the 'long tail' of neuroscience

Constructing Data Curation Profiles

Scholarship and disciplinary practices

The analytic potential of scientific data: Understanding re‐use value