Kimberly Douglass

Bio: Kimberly Douglass is an academic researcher from University of Tennessee. The author has contributed to research in topics: Physics & Data management. The author has an hindex of 7, co-authored 12 publications receiving 1166 citations. Previous affiliations of Kimberly Douglass include University UCINF.

Data Sharing by Scientists: Practices and Perceptions

Abstract: Background: Scientific research in the 21st century is more data intensive and collaborative than in the past. It is important to study the data practices of researchers – data accessibility, discovery, re-use, preservation and, particularly, data sharing. Data sharing is a valuable part of the scientific method allowing for verification of results and extending research from prior results. Methodology/Principal Findings: A total of 1329 scientists participated in this survey exploring current data sharing practices and perceptions of the barriers and enablers of data sharing. Scientists do not make their data electronically available to others for various reasons, including insufficient time and lack of funding. Most respondents are satisfied with their current processes for the initial and short-term parts of the data or research lifecycle (collecting their research data; searching for, describing or cataloging, analyzing, and short-term storage of their data) but are not satisfied with long-term data preservation. Many organizations do not provide support to their researchers for data management both in the shortand long-term. If certain conditions are met (such as formal citation and sharing reprints) respondents agree they are willing to share their data. There are also significant differences and approaches in data management practices based on primary funding agency, subject discipline, age, work focus, and world region. Conclusions/Significance: Barriers to effective data sharing and preservation are deeply rooted in the practices and culture of the research process as well as the researchers themselves. New mandates for data management plans from NSF and other federal agencies and world-wide attention to the need to share and preserve data could lead to changes. Large scale programs, such as the NSF-sponsored DataNET (including projects like DataONE) will both bring attention and resources to the issue and make it easier for scientists to apply sound data management principles.

Overview of the Instrumentation for the Dark Energy Spectroscopic Instrument

Abstract: The Dark Energy Spectroscopic Instrument (DESI) embarked on an ambitious 5 yr survey in 2021 May to explore the nature of dark energy with spectroscopic measurements of 40 million galaxies and quasars. DESI will determine precise redshifts and employ the baryon acoustic oscillation method to measure distances from the nearby universe to beyond redshift z > 3.5, and employ redshift space distortions to measure the growth of structure and probe potential modifications to general relativity. We describe the significant instrumentation we developed to conduct the DESI survey. This includes: a wide-field, 3.°2 diameter prime-focus corrector; a focal plane system with 5020 fiber positioners on the 0.812 m diameter, aspheric focal surface; 10 continuous, high-efficiency fiber cable bundles that connect the focal plane to the spectrographs; and 10 identical spectrographs. Each spectrograph employs a pair of dichroics to split the light into three channels that together record the light from 360–980 nm with a spectral resolution that ranges from 2000–5000. We describe the science requirements, their connection to the technical requirements, the management of the project, and interfaces between subsystems. DESI was installed at the 4 m Mayall Telescope at Kitt Peak National Observatory and has achieved all of its performance goals. Some performance highlights include an rms positioner accuracy of better than 0.″1 and a median signal-to-noise ratio of 7 of the [O ii] doublet at 8 × 10−17 erg s−1 cm−2 in 1000 s for galaxies at z = 1.4–1.6. We conclude with additional highlights from the on-sky validation and commissioning, key successes, and lessons learned.

Managing scientific data as public assets: Data sharing practices and policies among full-time government employees

Abstract: This paper examines how scientists working in government agencies in the U.S. are reacting to the “ethos of sharing” government-generated data. For scientists to leverage the value of existing government data sets, critical data sets must be identified and made as widely available as possible. However, government data sets can only be leveraged when policy makers first assess the value of data, in much the same way they decide the value of grants for research outside government. We argue that legislators should also remove structural barriers to interoperability by funding technical infrastructure according to issue clusters rather than administrative programs. As developers attempt to make government data more accessible through portals, they should consider a range of other nontechnical constraints attached to the data. We find that agencies react to the large number of constraints by mostly posting their data on their own websites only rather than in data portals that can facilitate sharing. Despite the nontechnical constraints, we find that scientists working in government agencies exercise some autonomy in data decisions, such as data documentation, which determine whether or not the data can be widely shared. Fortunately, scientists indicate a willingness to share the data they collect or maintain. However, we argue further that a complete measure of access should also consider the normative decisions to collect (or not) particular data.

Framing Librarianship in the Academy: an Analysis Using Bolman and Deal's Model of Organizations

Abstract: Since the earliest days of the profession, academic librarians have attempted to reconcile their status within the academy. This project takes a new approach to this effort by using Lee Bolman and Terrence Deal’s “Four Frames” model to analyze the issues. To more closely examine the dynamics, tensions, and implications associated with librarians’ professional status within the academy, we discuss the role of the academic librarian in the context of each of Bolman and Deal’s frames: Structural, Human Resource, Political, and Symbolic. In this discussion, we demonstrate that librarians are positioned to address human resource (as defined by Bolman and Deal), political, and symbolic factors contributing to their status within the academy. Also, while we establish that the relationship between library faculty and disciplinary faculty plays a role in library faculty status, we conclude that library faculty status is constructed by a number of forces. We further conclude that many of the political and symbolic conditions experienced by librarians are rooted in structural and human resource factors controlled by upper-level administration in both libraries and the universities.

Data reuse and the open data citation advantage

Abstract: Background. Attribution to the original contributor upon reuse of published data is important both as a reward for data creators and to document the provenance of research findings. Previous studies have found that papers with publicly available datasets receive a higher number of citations than similar studies without available data. However, few previous analyses have had the statistical power to control for the many variables known to predict citation rate, which has led to uncertain estimates of the “citation benefit”. Furthermore, little is known about patterns in data reuse over time and across datasets. Method and Results. Here, we look at citation rates while controlling for many known citation predictors and investigate the variability of data reuse. In a multivariate regression on 10,555 studies that created gene expression microarray data, we found that studies that made data available in a public repository received 9% (95% confidence interval: 5% to 13%) more citations than similar studies for which the data was not made available. Date of publication, journal impact factor, open access status, number of authors, first and last author publication history, corresponding author country, institution citation history, and study topic were included as covariates. The citation benefit varied with date of dataset deposition: a citation benefit was most clear for papers published in 2004 and 2005, at about 30%. Authors published most papers using their own datasets within two years of their first publication on the dataset, whereas data reuse papers published by third-party investigators continued to accumulate for at least six years. To study patterns of data reuse directly, we compiled 9,724 instances of third party data reuse via mention of GEO or ArrayExpress accession numbers in the full text of papers. The level of third-party data use was high: for 100 datasets deposited in year 0, we estimated that 40 papers in PubMed reused a dataset by year 2, 100 by year 4, and more than 150 data reuse papers had been published by year 5. Data reuse was distributed across a broad base of datasets: a very conservative estimate found that 20% of the datasets deposited between 2003 and 2007 had been reused at least once by third parties. Conclusion. After accounting for other factors affecting citation rate, we find a robust citation benefit from open data, although a smaller one than previously reported. We conclude there is a direct effect of third-party data reuse that persists for years beyond the time when researchers have published most of the papers reusing their own data. Other factors that may also contribute to the citation benefit are considered. We further conclude that, at least for gene expression microarray data, a substantial fraction of archived datasets are reused, and that the intensity of dataset reuse has been steadily increasing since 2003.

Advances in restoration ecology: rising to the challenges of the coming decades

Abstract: Simultaneous environmental changes challenge biodiversity persistence and human wellbeing. The science and practice of restoration ecology, in collaboration with other disciplines, can contribute to overcoming these challenges. This endeavor requires a solid conceptual foundation based in empirical research which confronts, tests and influences theoretical developments. We review conceptual developments in restoration ecology over the last 30 years. We frame our review in the context of changing restoration goals which reflect increased societal awareness of the scale of environmental degradation and the recognition that inter-disciplinary approaches are needed to tackle environmental problems. Restoration ecology now encompasses facilitative interactions and network dynamics, trophic cascades, and above- and below ground linkages. It operates in a non-equilibrium, alternative states framework, at the landscape scale, and in response to changing environmental, economic and social conditions. Progress has been marked by conceptual advances in the fields of trait-environment relationships, community assembly, and understanding the links between biodiversity and ecosystem functioning. Conceptual and practical advances have been enhanced by applying evolving technologies, including treatments to increase seed germination and overcome recruitment bottlenecks, high throughput DNA sequencing to elucidate soil community structure and function, and advances in satellite technology and GPS tracking to monitor habitat use. The synthesis of these technologies with systematic reviews of context dependencies in restoration success, model based analyses and consideration of complex socio-ecological systems will allow generalizations to inform evidence based interventions. Ongoing challenges include setting realistic, socially acceptable goals for restoration under changing environmental conditions, and prioritizing actions in an increasingly space-competitive world. Ethical questions also surround the use of genetically modified material, translocations, taxon substitutions, and de-extinction, in restoration ecology. Addressing these issues, as the Ecological Society of America looks to its next century, will require current and future generations of researchers and practitioners, including economists, engineers, philosophers, landscape architects, social scientists and restoration ecologists, to work together with communities and governments to rise to the environmental challenges of the coming decades.

Altmetrics: Value all research products

Abstract: A new funding policy by the US National Science Foundation represents a sea-change in how researchers are evaluated, says Heather Piwowar.

Scholarly use of social media and altmetrics: A review of the literature

Abstract: Social media has become integrated into the fabric of the scholarly communication system in fundamental ways, principally through scholarly use of social media platforms and the promotion of new indicators on the basis of interactions with these platforms. Research and scholarship in this area has accelerated since the coining and subsequent advocacy for altmetrics—that is, research indicators based on social media activity. This review provides an extensive account of the state-of-the art in both scholarly use of social media and altmetrics. The review consists of 2 main parts: the first examines the use of social media in academia, reviewing the various functions these platforms have in the scholarly communication process and the factors that affect this use. The second part reviews empirical studies of altmetrics, discussing the various interpretations of altmetrics, data collection and methodological limitations, and differences according to platform. The review ends with a critical discussion of the implications of this transformation in the scholarly communication system.

If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology.

Abstract: Research on practices to share and reuse data will inform the design of infrastructure to support data collection, management, and discovery in the long tail of science and technology. These are research domains in which data tend to be local in character, minimally structured, and minimally documented. We report on a ten-year study of the Center for Embedded Network Sensing (CENS), a National Science Foundation Science and Technology Center. We found that CENS researchers are willing to share their data, but few are asked to do so, and in only a few domain areas do their funders or journals require them to deposit data. Few repositories exist to accept data in CENS research areas.. Data sharing tends to occur only through interpersonal exchanges. CENS researchers obtain data from repositories, and occasionally from registries and individuals, to provide context, calibration, or other forms of background for their studies. Neither CENS researchers nor those who request access to CENS data appear to use external data for primary research questions or for replication of studies. CENS researchers are willing to share data if they receive credit and retain first rights to publish their results. Practices of releasing, sharing, and reusing of data in CENS reaffirm the gift culture of scholarship, in which goods are bartered between trusted colleagues rather than treated as commodities.