Microtubules are integral to neuronal development and function. They endow cells with polarity, shape, and structure, and their extensive surface area provides substrates for intracellular trafficking and scaffolds for signaling molecules. Consequently, microtubule polymerization dynamics affect not only structural features of the cell but also the subcellular localization of proteins that can trigger intracellular signaling events. In the nematode Caenorhabditis elegans , the processes of touch receptor neurons are filled with a bundle of specialized large-diameter microtubules. We find that conditions that disrupt these microtubules (loss of either the MEC-7 β-tubulin or MEC-12 α-tubulin or growth in 1 mM colchicine) cause a general reduction in touch receptor neuron (TRN) protein levels. This reduction requires a p38 MAPK pathway (DLK-1, MKK-4, and PMK-3) and the transcription factor CEBP-1. Cells may use this feedback pathway that couples microtubule state and MAPK activation to regulate cellular functions.

Data “publication” seeks to appropriate the prestige of authorship in the peer-reviewed literature to reward researchers who create useful and well-documented datasets. The scholarly communication community has embraced data publication as an incentive to document and share data. But, numerous new and ongoing experiments in implementation have not yet resolved what a data publication should be, when data should be peer-reviewed, or how data peer review should work. While researchers have been surveyed extensively regarding data management and sharing, their perceptions and expectations of data publication are largely unknown. To bring this important yet neglected perspective into the conversation, we surveyed ∼ 250 researchers across the sciences and social sciences– asking what expectations“data publication” raises and what features would be useful to evaluate the trustworthiness, evaluate the impact, and enhance the prestige of a data publication. We found that researcher expectations of data publication center on availability, generally through an open database or repository. Few respondents expected published data to be peer-reviewed, but peer-reviewed data enjoyed much greater trust and prestige. The importance of adequate metadata was acknowledged, in that almost all respondents expected data peer review to include evaluation of the data’s documentation. Formal citation in the reference list was affirmed by most respondents as the proper way to credit dataset creators. Citation count was viewed as the most useful measure of impact, but download count was seen as nearly as valuable. These results offer practical guidance for data publishers seeking to meet researcher expectations and enhance the value of published data.

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.

We correlate chromosomal changes in medulloblastomas with histologic subtype, reporting the analysis of 33 medulloblastoma specimens by comparative genomic hybridization, and a subset by fluorescence in situ hybridization. Of the 33 tumors, 5 were desmoplastic/nodular, 10 were histologically classic, and 18 were large cell/anaplastic. Chromosomal gains and losses were more common in anaplastic medulloblastomas than in non‐anaplastic ones. We identified 4 medulloblastomas with c‐myc amplification and 5 medulloblastomas with N‐myc amplification; all 9 were of the large cell/anaplastic subtype. Additional regions with high level gains included 2q14‐22, 3p23, 5p14‐pter, 8q24, 9p22‐23, 10p12‐pter, 12q24, 12p11‐12, 17p11‐12, and Xp11. The majority of these high level gains occurred in anaplastic cases. We also found loss of chromosome 17p in 7 large cell/anaplastic cases but no non‐anaplastic medulloblastomas. Finally, we detected a significantly increased overall number of chromosomal alterations in large cell/anaplastic medulloblastomas (6.8/case) compared to non‐anaplastic ones (3.3/case). These findings support an association between myc oncogene amplification, 17p loss, and large cell/anaplastic histology.

The movement to bring datasets into the scholarly record as first class research products (validated, preserved, cited, and credited) has been inching forward for some time, but now the pace is quickening. As data publication venues proliferate, significant debate continues over formats, processes, and terminology. Here, we present an overview of data publication initiatives underway and the current conversation, highlighting points of consensus and issues still in contention. Data publication implementations differ in a variety of factors, including the kind of documentation, the location of the documentation relative to the data, and how the data is validated. Publishers may present data as supplemental material to a journal article, with a descriptive "data paper," or independently. Complicating the situation, different initiatives and communities use the same terms to refer to distinct but overlapping concepts. For instance, the term means that the data is publicly available and citable to published virtually everyone, but it may or may not imply that the data has been peer-reviewed. In turn, what is meant by data peer review is far from defined; standards and processes encompass the full range employed in reviewing the literature, plus some novel variations. Basic data citation is a point of consensus, but the general agreement on the core elements of a dataset citation frays if the data is dynamic or part of a larger set. Even as data publication is being defined, some are looking past publication to other metaphors, notably "data as software," for solutions to the more stubborn problems.This article is included in the Science Policy gateway. ResearchThis article is included in the Data: Use and Reuse collection. Amendments from Version 2This version no longer presents three models for data publication based on documentation. Instead, we treat documentation as an essential feature and discuss three forms of documentation in parallel with forms of availability, citation, and validation. The figure has been updated to reflect this reorganization.Numerous minor additions, corrections and clarifications were made throughout in response to referee and reader comments. Most significantly, the discussions of paper-independent documentation and validation have been expanded, as has the concluding "beyond data publication". See referee reports REVISED What does data publication mean?The idea that researchers should share data to advance knowledge and promote the common good is an old one, but in recent years the conversation has shifted from sharing data to publishing data 1-3 . This shift in language stems from the conviction that datasets should join the scholarly record and be afforded the same first-class status as traditional research products like journal articles 4,5 . While many in the scholarly communication community share this goal, different people and organizations often refer to different things with the phrase data publication. Lawrence et al. (2011) define formal data Publication (upper-c...