Alberto Pepe

How the Scientific Community Reacts to Newly Submitted Preprints: Article Downloads, Twitter Mentions, and Citations

We analyze the online response to the preprint publication of a cohort of 4,606 scientific articles submitted to the preprint database arXiv.org between October 2010 and May 2011. We study three forms of responses to these preprints: downloads on the arXiv.org site, mentions on the social media site Twitter, and early citations in the scholarly record. We perform two analyses. First, we analyze the delay and time span of article downloads and Twitter mentions following submission, to understand the temporal configuration of these reactions and whether one precedes or follows the other. Second, we run regression and correlation tests to investigate the relationship between Twitter mentions, arXiv downloads, and article citations. We find that Twitter mentions and arXiv downloads of scholarly articles follow two distinct temporal patterns of activity, with Twitter mentions having shorter delays and narrower time spans than arXiv downloads. We also find that the volume of Twitter mentions is statistically correlated with arXiv downloads and early citations just months after the publication of a preprint, with a possible bias that favors highly mentioned articles.

Ten Simple Rules for the Care and Feeding of Scientific Data

In the early 1600s, Galileo Galilei turned a telescope toward Jupiter. In his log book each night, he drew to-scale schematic diagrams of Jupiter and some oddly moving points of light near it. Galileo labeled each drawing with the date. Eventually he used his observations to conclude that the Earth orbits the Sun, just as the four Galilean moons orbit Jupiter. History shows Galileo to be much more than an astronomical hero, though. His clear and careful record keeping and publication style not only let Galileo understand the solar system, they continue to let anyone understand how Galileo did it. Galileos notes directly integrated his data (drawings of Jupiter and its moons), key metadata (timing of each observation, weather, and telescope properties), and text (descriptions of methods, analysis, and conclusions). Critically, when Galileo included the information from those notes in Sidereus Nuncius, this integration of text, data, and metadata was preserved, as shown in Figure 1. Galileos work advanced the “Scientific Revolution,” and his approach to observation and analysis contributed significantly to the shaping of todays modern “scientific method”.

On the relationship between the structural and socioacademic communities of a coauthorship network

This article presents a study that compares detected structural communities in a coauthorship network to the socioacademic characteristics of the scholars that compose the network. The coauthorship network was created from the bibliographic record of a multi-institution, interdisciplinary research group focused on the study of sensor networks and wireless communication. Four different community detection algorithms were employed to assign a structural community to each scholar in the network: leading eigenvector, walktrap, edge betweenness and spinglass. Socioacademic characteristics were gathered from the scholars and include such information as their academic department, academic affiliation, country of origin, and academic position. A Pearson’s χ2test, with a simulated Monte Carlo, revealed that structural communities best represent groupings of individuals working in the same academic department and at the same institution. A generalization of this result suggests that, even in interdisciplinary, multi-institutional research groups, coauthorship is primarily driven by departmental and institutional affiliation.

Know thy sensor: trust, data quality, and data integrity in scientific digital libraries

For users to trust and interpret the data in scientific digital libraries, they must be able to assess the integrity of those data. Criteria for data integrity vary by context, by scientific problem, by individual, and a variety of other factors. This paper compares technical approaches to data integrity with scientific practices, as a case study in the Center for Embedded Networked Sensing (CENS) in the use of wireless, in-situ sensing for the collection of large scientific data sets. The goal of this research is to identify functional requirements for digital libraries of scientific data that will serve to bridge the gap between current technical approaches to data integrity and existing scientific practices.

A Measure of Total Research Impact Independent of Time and Discipline

Authorship and citation practices evolve with time and differ by academic discipline. As such, indicators of research productivity based on citation records are naturally subject to historical and disciplinary effects. We observe these effects on a corpus of astronomer career data constructed from a database of refereed publications. We employ a simple mechanism to measure research output using author and reference counts available in bibliographic databases to develop a citation-based indicator of research productivity. The total research impact (tori) quantifies, for an individual, the total amount of scholarly work that others have devoted to his/her work, measured in the volume of research papers. A derived measure, the research impact quotient (riq), is an age-independent measure of an individuals research ability. We demonstrate that these measures are substantially less vulnerable to temporal debasement and cross-disciplinary bias than the most popular current measures. The proposed measures of research impact, tori and riq, have been implemented in the Smithsonian/NASA Astrophysics Data System.

Digital libraries for scientific data discovery and reuse: from vision to practical reality

Science and technology research is becoming not only more distributed and collaborative, but more highly instrumented. Digital libraries provide a means to capture, manage, and access the data deluge that results from these research enterprises. We have conducted research on data practices and participated in developing data management services for the Center for Embedded Networked Sensing since its founding in 2002 as a National Science Foundation Science and Technology Center. Over the course of eight years, our digital library strategy has shifted dramatically in response to changing technologies, practices, and policies. We report on the development of several DL systems and on the lessons learned, which include the difficulty of anticipating data requirements from nascent technologies, building systems for highly diverse work practices and data types, the need to bind together multiple single-purpose systems, the lack of incentives to manage and share data, the complementary nature of research and development in understanding practices, and sustainability.

The relationship between acquaintanceship and coauthorship in scientific collaboration networks

This article examines the relationship between acquaintanceship and coauthorship patterns in a multi-disciplinary, multi-institutional, geographically distributed research center. Two social networks are constructed and compared: a network of coauthorship, representing how researchers write articles with one another, and a network of acquaintanceship, representing how those researchers know each other on a personal level, based on their responses to an online survey. Statistical analyses of the topology and community structure of these networks point to the importance of small-scale, local, personal networks predicated upon acquaintanceship for accomplishing collaborative work in scientific communities.

The Use of Microblogging for Field-Based Scientific Research

Documenting the context in which data are collected is an integral part of the scientific research lifecycle. In field-based research, contextual information provides a detailed description of scientific practices and thus enables data interpretation and reuse. For field data, losing contextual information often means losing the data altogether. Yet, documenting the context of distributed, collaborative, field-based research can be a significant challenge due to the unpredictable nature of real-world settings and to the high degree of variability in data collection methods and scientific practices of different researchers. In this article, we propose the use of microblogging as a mechanism to support collection, ingestion, and publication of contextual information about the variegated digital artifacts that are produced in field research. We perform interviews with scholars involved in field-based environmental and urban sensing research, to determine the extent of adoption of Twitter and similar microblogging platforms and their potential use for field-specific research applications. Based on the results of these interviews as well as participant observation of field activities, we present the design, development, and pilot evaluation of a microblogging application integrated with an existing data collection platform on a handheld device. We investigate whether microblogging accommodates the variable and unpredictable nature of highly mobile research and whether it represents a suitable mechanism to document the context of field research data early in the scientific information lifecycle.

The Dilated Triple

The basic unit of meaning on the Semantic Web is the RDF statement, or triple, which combines a distinct subject, predicate and object to make a definite assertion about the world. A set of triples constitutes a graph, to which they give a collective meaning. It is upon this simple foundation that the rich, complex knowledge structures of the Semantic Web are built. Yet the very expressiveness of RDF, by inviting comparison with real-world knowledge, highlights a fundamental shortcoming, in that RDF is limited to statements of absolute fact, independent of the context in which a statement is asserted. This is in stark contrast with the thoroughly context-sensitive nature of human thought. The model presented here provides a particularly simple means of contextualizing an RDF triple by associating it with related statements in the same graph. This approach, in combination with a notion of graph similarity, is sufficient to select only those statements from an RDF graph which are subjectively most relevant to the context of the requesting process.