Difference between revisions of "Develop proposal for special issue"

Revision as of 17:18, 17 April 2015

Background: Why a Special Issue on Geoscience Papers of the Future?

Include here our discussion for the vision

Motivation: The EarthCube Initiative and the GeoSoft Project

Include here background about EarthCube and GeoSoft from the project web site

What is a GPF

Include here our discussion of what is a GPF

The challenges of creating GPFs

The articles in this special issue will reflect the current best practice for generating a Geoscience Paper of the Future. The authors will discuss the challenges that they have encountered, including limitations and availability of data publishing repositories, difficulties in describing software infrastructure, constraints posed by projects and collaborators on the release of data and software, and open questions about what aspects of the research process should be published.

Related work

Include here the related work we have discussed

Papers to be included

The papers included will be by invitation only. This is analogous to special issues of journals that are based on a conference or event, where all the papers presented are invited among the event participants. The Geoscience Paper of the Future activity is of a similar nature, and the papers we will invite are from the participants of that activity. This will make the special issue more appealing as there will be a consistent structure across the papers.

For each planned submission, we describe here:

• Authors and affiliations
• Keywords of research area
• Tentative title
• Short abstract
• Challenge: this can be Reproducibility (i.e., documenting and reproducing previously published results), Dark Code (i.e., describing and sharing code integral to the presented results), Sharing Big Data (i.e. making available large datasets), and Transferability (i.e., updating a previously-used method to a new version of software, etc.).
• Relationship to other publications: is the article based on a previously published article? is it new content? If previously published, this also indicates the percentage of new work presented.
• Pointer to the wiki page that documents the article
• Expected submission date

[David 2015]

• Authors and affiliations: Cedric David
• Keywords of research area: Hydrology, Rivers, Modeling, Testing, Reproducibility.
• Tentative title: Going beyond triple-checking, allowing for peace of mind in community model development.
• Short abstract: The development of computer models in the general field of geoscience is often made incrementally over many years. Endeavors that generally start on one single researcher's own machine evolve over time into software that are often much larger than was initially anticipated. Looking at years of building on their computer code, sometimes without much training in computer science, geoscience software developers can easily experience an overwhelming sense of incompetence when contemplating ways to further community usage of their software. How does one allow others to use their code? How can one foster survival of their tool? How could one possibly ensure the scientific integrity of ongoing developments including those made by others? Common issues faced by geoscience developers include selecting a license, learning how to track and document past and ongoing changes, choosing a software repository, and allowing for community development. This paper provides a brief summary of experience with the three former steps of software growth by focusing on the almost decade-long code development of a river routing model. The core of this study, however, focuses on reproducing previously-published experiments. This step is highly repetitive and can therefore benefit greatly from automation. Additionally, enabling automated software testing can arguably be considered the final step for sustainable software sharing, by allowing the main software developer to let go of a mental block considering scientific integrity. Creating tools to automatically compare the results of an updated version of a software with those of previous studies can not only save the main developer's own time, it can also empower other researchers to in their ability to check and justify that their potential additions have retained scientific integrity.
• Challenge: Reproducibility; Sharing Big Data. Ensure that updates to an existing model are able to reproduce a series of simulations published previously.
• Relationship to other publications: This research is related to past and ongoing development of the Routing Application for Parallel computatIon of Discharge (RAPID). The primary focus of this paper is to allow automated reproducibility of at least the first RAPID publication. The scientific subject of this GPF differs from the article(s) to be reproduced as its focus is on development of automatic testing methods. In that regard, the paper is expected to be 95% new.
• Pointer to the wiki page that documents the article: Page
• Expected submission date:

[Demir 2015]

• Authors and affiliations: Ibrahim Demir
• Keywords of research area: hydrological network, optimization, network representation, database query
• Tentative title: Analysis and Optimization of Hydrological Network Database Representation Methods for Fast Access and Query in Web-based System
• Short abstract: Web based systems allow users to delineate watersheds on interactive map environments using server side processing. With increasing resolution of hydrological networks, optimized methods for storage of network representation in databases, and efficient queries and actions on the river network structure become critical. This paper presents a detailed study on analysis of widely used methods for representing hydrological networks in relational databases, and benchmarking common queries and modifications on the network structure using these methods. The analysis has been applied to the hydrological network of Iowa utilizing 90m DEM and 600,000 network nodes. The application results indicate that the representation methods provide massive improvements on query times and storage of network structure in the database. Suggested method allows watershed delineation tools running on client-side with desktop-like performance.
• Challenge: Reproducibility, Transferability; Some of the internal steps to prepare data might require long computation time and different software environments.
• Relationship to other publications: The article is based on a new study
• Pointer to the wiki page that documents the article: Page
• Expected submission date:

Fulweiler: [Fulweiler, Emery, and Maguire 2015]

• Authors and affiliations: Robinson W. Fulweiler^1,2, Hollie E. Emery¹, and Timothy J. Maguire² (1: Department of Earth and Environment, Boston University; 2: Department of Biology, Boston University.)
• Keywords of research area: biogeochemistry, climate change, benthic-pelagic coupling, long-term data, reproducibility
• Tentative title: What can we learn from a decade of directly measured sediment di-nitrogen gas fluxes?
• Short abstract: Long-term data sets provide unique opportunities to examine temporal variability of important ecosystem processes. Unfortunately, these data sets are rare and curating them is a real challenge. Additionally, it can be difficult to publish them in a timely manner. However, if we wish to make our data available for interested parties (e.g., students, scientists, managers, etc.) then we need to provide mechanisms that allow others to access the data, reproduce the results, and see updates as they become available. Here will use a long-term data set of directly measured net sediment N2 fluxes to assess how a temperate estuary changes overtime. Specifically, we will address how environmental factors alter the balance between sediment denitrification (nitrogen removal) and sediment nitrogen fixation (nitrogen addition). This balance is essential to understand if we wish to better manage coastal systems and the anthropogenic nitrogen loads they receive.
• Challenge: Reproducibility and efficient ways to update long-term data. This paper will address how to reproduce the key figures from the paper that was published in Oceanography in 2014. I also want to address how we deal with long-term data sets and the intermittent collection of data. For example, in the paper we will use here we published 9 years’ worth of data – a collection of previously published data plus data from additional measurements over seven years. Now I have another year – do I publish another paper? Do I wait for another seven years? The former seems to short and the latter too long. But having a way to update the data and the figures – would be really powerful. A much more timely and relevant set of information.
• Relationship to other publications: The majority of these data were previously published (Fulweiler and Heiss 2014) and a small amount of additional previously un-published data will be included here. The point of this paper is to develop a framework to describe how we analyzed the data and to provide code that will allow others to reproduce our results using our data or data that they acquire. Additionally, if possible we will share these data and then have it updated as we collect new data so that interested parties can see how the system we are study is changing overtime.
• Pointer to the wiki page that documents the article: Page
• Expected submission date: June 2015.

Goodall: [Essawy, Goodall, Billah, and Xu 2015]

• Authors and affiliations: Bakinam T. Essawy, Jonathan L. Goodall, Mirza Billah, and Hao Xu, Department of Civil and Environmental Engineering, University of Virginia.
• Keywords of research area: Hydrology, Automating workflows, Reproducibility, sharing
• Tentative title: Post-processing Workflows Using Data Grids to Support Hydrologic Modeling
• Short abstract: Data grids are architectures that allow scientists to access and share large data sets that are geographically distributed on the Internet, but appear to the scientist as a single file management system. Data grids are useful for scientific communities, like hydrology, that rely on multiple resource providers and data resources that are distributed across the Internet. One data grid technology is the Integrated Rule-Oriented System (iRODS). This paper leverages iRODS and demonstrates how it can be used to access distributed data, encapsulate hydrological modeling knowledge as workflows, and interoperate with other community-driven cyberinfrastructures. Included within iRODS is the concept of Workflow Structured Objects (WSO) that can be used to automate data processing using data collections stored within iRODS. A use case is presented that demonstrates creating WSOs that automate the creation of data visualizations from large model output collections. By co-locating the workflow used to create the visualization with the data collection, the use case demonstrates how data grid technology aids in reuse, reproducibility, and sharing of workflows within scientific communities. The use case leverages output from a hydrologic model (the Variable Infiltration Capacity model) for the Carolinas region of the US, and is part of a larger effort under the DataNet Federation Consortium (DFC) project that aims to demonstrate data and computational interoperability across scientific communities.
• Challenge: This paper discusses how to automate workflows to make it reproducible and sharable by other disciplines.
• Relationship to other publications: This article is an extension of another article that is currently under review.
• Pointer to the wiki page that documents the article: Page
• Expected submission date:

Karlstrom: [Loh and Karlstrom 2015]

• Authors and affiliations: Lay Kuan Loh¹ and Leif Karlstrom² (1: Department of Electrical and Computer Engineering, Carnegie Mellon University; 2: Department of Geological Sciences, University of Oregon)
• Keywords of research area: Spatial clustering, Eigenvector selection, Entropy Ranking, Cascades Volcanic Region, Afar Depression, Tharsis provonce
• Tentative title: Characterization of volcanic vent distributions using spectral clustering with eigenvector selection and entropy ranking
• Short abstract: Volcanic vents on the surface of Earth and other planets often appear in groups that exhibit spatial patterning. Such vent distributions reflect complex interplay between time-evolving mechanical controls on the pathways of magma ascent, background tectonic stresses, and unsteady supply of rising magma. With the ultimate aim of connecting surface vent distributions with the dynamics of magma ascent, we have developed a clustering method to quantify spatial patterns in vents. Clustering is typically used in exploratory data analysis to identify groups with similar behavior by partitioning a dataset into clusters that share similar attributes. Traditional clustering algorithms that work well on simple point-cloud type synthetic datasets generally do not scale well the real-world data we are interested in, where there are poor boundaries between clusters and much ambiguity in cluster assignments. We instead use a spectral clustering algorithm with eigenvector selection based on entropy ranking based off work from Zhao et al 2010 that outperforms traditional spectral clustering algorithms in choosing the right number of clusters for point data. We benchmark this algorithm on synthetic vent data with increasingly complex spatial distributions, to test the ability to accurately cluster vent data with variable spatial density, skewness, number of clusters, and proximity of clusters. We then apply our algorithm to several real-world datasets from the Cascades, Afar Depression and Mars.
• Challenge: Reproducibility (i.e., Quantifying clustering); We plan to study how varying the statistical distribution, density, skewness, background noise, number of clusters, proximity of clusters, and combinations of any of these factors affects the performance of our algorithm. We test it against man-made and real world datasets.
• Relationship to other publications: New content, but one of the databases we are studying in the paper (Cascades Volcanic Range) would be based off a different paper we are preparing and planning to submit earlier.
• Pointer to the wiki page that documents the article: Page
• Expected submission date: June 2015

Lee: [Lee, Boustani, and Mattmann 2015]

• Authors and affiliations: Kyo Lee, Maziyar Boustani and Chris Mattmann, Jet Propulsion Laboratory
• Keywords of research area:North American regional climate, regional climate model evaluation system, Open Climate Workbench,
• Tentative title: Evaluation of simulated temperature, precipitation, cloud fraction and insolation over the conterminous United States using Regional Climate Model Evaluation System
• Short abstract:This study describes the detailed process of evaluating model fidelity in simulating four key climate variables, surface air temperature, precipitation, cloud fraction and insolation and their covariability over the conterminous United States region. Regional Climate Model Evaluation System (RCMES), a suite of public database and open-source software package, provides both observational datasets and data processors useful for evaluating any climate models. In this paper, we provide a clear and easy-to-follow workflow of RCMES to replicate published papers evaluating North American Regional Climate Change Assessment Program (NARCCAP) regional climate model (RCM) hindcast simulations using observations from variety of sources.
• Challenge:Big Data Sharing, Dark Code; Sharing big data, better documenting source codes, encouraging climate science community to use RCMES
• Relationship to other publications: Kim et al. 2013, Lee et al. 2014
• Pointer to the wiki page that documents the article: Page
• Expected submission date:End of June 2015

Mills: [Reese, Mills, Witmer, and Morse 2015]

• Authors and affiliations: Brandi Kiel Reese, Texas A&M Corpus Christi

Brandi Kiel Reese¹, Heath J. Mills², Angela D. Witmer³, John W. Morse⁴ (1: Texas A&M University Corpus Christi, Department of Life Sciences, Corpus Christi, TX 78412; 2: University of Houston Clear Lake, Division of Natural Sciences, Houston, TX 77058; 3: Georgia Southern University, Department of Biology, Statesboro, GA 30458; 4: Texas A&M University, Department of Oceanography, College Station, TX 77843

• Keywords of research area:
• Tentative title:Iron and Sulfur Cycling Biogeography Using Advanced Geochemical and Molecular Analyses
• Short abstract:

Most biogeochemical studies describe microbial ecology using only aqueous geochemistry. However these studies neglect to characterize the bioavailable, solid-phase portions as it relates to the local environment. Solid phase species of key elements, including sulfur and iron, are typically underestimated sources for microbial activity. The objective of this study was to determine spatial and temporal variability of the benthic ecosystem through biogeochemical and molecular analysis while focusing on the sulfur and iron cycles. During this study, multiple 20 cm sediment cores were collected from three northern Gulf of Mexico hypoxic zone locations, each in 20 m water depth. Aqueous and solid phase iron and sulfur compounds were analyzed in combination with molecular microbial characterization, and the degree of pyritization was determined. Sediments within this study had geochemically distinct profiles at all three locations in the forms of iron or sulfur species, despite bulk sediment chemistry appearing functionally similar (i.e., the concentrations of general chemical species did not vary greatly). Variations in iron and sulfur bioavailability altered the microbial ecology, both in terms of structure and function. In turn, community activity can contribute to small spatial scale changes in the geochemistry providing a potential for geographic and geochemical isolation. Therefore, localized feedback loops between available geochemistry and the microbial community can result in population divergence, exhibiting biogeography with sediments.

• Challenge: Reproducibility; Dark Code. This paper will develop and document a new pipeline to analyze a combined and robust genetic and geochemical data set. New, reproducible methods will be highlighted in this manuscript to help others better analyze similar data sets. There is a general lack of guidance within this field for such challenges. This manuscript will be unique and helpful from an analysis standpoint as well as for the science being presented.
• Relationship to other publications: Original Manuscript
• Pointer to the wiki page that documents the article: Page
• Expected submission date:

[Oh 2015]

• Authors and affiliations: Ji-Hyun Oh Jet Propulsion Laboratory/University of Southern California
• Keywords of research area: Tropical Meteorology, Madden-Julian Oscillation, Momentum budget analysis
• Tentative title: Tools for computing momentum budget for the westerly wind event associated with the Madden-Julian Oscillation
• Short abstract:As one of the most pronounced modes of tropical intraseasonal variability, the Madden-Julian Oscillation (MJO) prominently connects global weather and climate, and serves as one of critical predictability sources for extended-range forecasting. The zonal circulation of the MJO is characterized by low-level westerlies (easterlies) in and to the west (east) of the convective center, respectively. The direction of zonal winds in the upper troposphere is opposite to that in the lower troposphere. In addition to the convective signal as an identifier of the MJO initiation, certain characteristics of the zonal circulation been used as a standard metric for monitoring the state of MJO and investigating features of the MJO and its impact on other atmospheric phenomena. This paper documents a tool for investigating the generation of low-level westerly winds during the MJO life cycle. The tool is used for the momentum budget analysis to understand the respective contributions of various processes involved in the wind evolution associated with the MJO using European Centre for Medium-Range Weather Forecasts operational analyses during Dynamics of the Madden–Julian Oscillation field campaign.
• Challenge: Reproducibility, Dark Code; This paper will cover how to reproduce two key figures from the paper that I recently submitted to Journal of Atmospheric Science. This will include detailed procedures related to generating the figures such as how/where to download data, how to transform the format of the data to be used as an input for my codes, and so on..
• Relationship to other publications: (is the article based on a previously published article? is it new content?) This article is related to the part of the paper submitted to Journal of Atmospheric Science.
• Pointer to the wiki page that documents the article: Page
• Expected submission date:

Pierce: [Pierce, Gentle, and Noll 2015]

• Authors and affiliations: Suzanne Pierce, John Gentle, and Daniel Noll (Texas Advanced Computing Center and Jackson School of Geosciences, The University of Texas at Austin; US Department of Energy)

• Keywords of research area: Decision Support Systems, Hydrogeology, Participatory Modeling, Data Fusion
• Tentative title: MCSDSS: An accessible platform and application to enable data fusion and interactive visualization for the Geosciences
• Short abstract:The MCSDSS application is an advanced example of interactive design that can lead to data fusion for science visualization, decision support applications, and education. What sets the tool apart is its firm underpinning in data, innovative new forms of interface design, and the reusable platform. A key advance is the creation of a framework that can be used to feed new data, videos maps, images, or formats of information into the application with relative ease.

• Challenge: Reproducibility, Dark Code; Fully document a new software application and framework using example case study data and tutorials; Creation of an interface that enables non-programmers to build out interactive visualizations for their data
• Relationship to other publications: This article is new content, the proof of concept idea was developed with DOE funding for a student competition and resulted in an initial implementation that was reported in the DOE competition report and a masters thesis for co-author Daniel Noll
• Pointer to the wiki page that documents the article: Page
• Expected submission date: mid- to late June 2015

[Pope 2015]

• Authors and affiliations: Allen Pope, National Snow and Ice Data Center, University of Colorado, Boulder
• Keywords of research area: Glaciology, Remote Sensing, Landsat 8, Polar Science
• Tentative title: Data and Code for Estimating and Evaluating Supraglacial Lake Depth With Landsat 8 and other Multispectral Sensors
• Short abstract: Supraglacial lakes play a significant role in glacial hydrological systems – for example, transporting water to the glacier bed in Greenland or leading to ice shelf fracture and disintegration in Antarctica. To investigate these important processes, multispectral remote sensing provides multiple methods for estimating supraglacial lake depth – either through single-band or band-ratio methods, both empirical and physically-based. Landsat 8 is the newest satellite in the Landsat series. With new bands, higher dynamic range, and higher radiometric resolution, the Operational Land Imager (OLI) aboard Landsat 8 has a lot of potential.

This paper will document the data and code used in processing in situ reflectance spectra and depth measurements to investigate the ability of Landsat 8 to estimate lake depths using multiple methods, as well as quantify improvements over Landsat 7’s ETM+. A workflow, data, and code are provided to detail promising methods as applied to Landsat 8 OLI imagery of case study areas in Greenland, allowing calculation of regional volume estimates using 2013 and 2014 summer-season imagery. Altimetry from WorldView DEMs are used to validate lake depth estimates. The optimal method for supraglacial lake depth estimation with Landsat 8 is shown to be an average of single band depths by red and panchromatic bands. With this best method, preliminary investigation of seasonal behavior and elevation distribution of lakes is also discussed and documented.

• Challenge: Reproducibility, Dark Code
• Relationship to other publications: Documenting and explaining the data and code behind the analysis and results presented in another paper.
• Pointer to the wiki page that documents the article: Page
• Expected submission date: Late June 2015

Tzeng: [Tzeng and Park 2015]

• Authors and affiliations: Mimi Tzeng, Brian Dzwonkowski (DISL); Kyeong Park (TAMU Galveston)
• Keywords of research area:physical oceanography, remote sensing
• Tentative title: Fisheries Oceanography of Coastal Alabama (FOCAL): A Subset of a Time-Series of Hydrographic and Current Data from a Permanent Moored Station Outside Mobile Bay (27 Jan to 18 May 2011)
• Short abstract:The Fisheries Oceanography in Coastal Alabama (FOCAL) program began in 2006 as a way for scientists at Dauphin Island Sea Lab (DISL) to study the natural variability of Alabama's nearshore environment as it relates to fisheries production. FOCAL provided a long-term baseline data set that included time-series hydrographic data from a permanent offshore mooring (ADCP, vertical thermister array and CTDs at surface and bottom) and shipboard surveys (vertical CTD profiles and water sampling), as well as monthly ichthyoplankton and zooplankton (depth-discrete) sample collections at FOCAL sites. The subset of data presented here are from the mooring, and includes a vertical array of thermisters, CTDs at surface and bottom, an ADCP at the bottom, and vertical CTD profiles collected at the mooring during maintenance surveys. The mooring is located at 30 05.410'N 88 12.694'W, 25 km southwest of the entrance to Mobile Bay. Temperature, salinity, density, depth, and current velocity data were collected at 20-minute intervals from 2006 to 2012. Other parameters, such as dissolved oxygen, are available for portions of the time series depending on which instruments were deployed at the time.
• Challenge: Dark Code, Reproducibility; My paper will be about the processing of data in a larger dataset, from which peer-reviewed papers have been written. The processing I did was not specific to any particular paper. I can point to an example paper that used some of the data from this dataset, that I processed, however all of the figures in the paper are composites that also include other data from elsewhere that I had nothing to do with (and it wouldn't be feasible to try to get hold of the other data within our timeframe).
• Relationship to other publications: A recent paper that used the part of the FOCAL data I'm documenting as the sample from the larger dataset: Dzwonkowski, Brian, Kyeong Park, Jungwoo Lee, Bret M. Webb, and Arnoldo Valle-Levinson. 2014. "Spatial variability of flow over a river-influenced inner shelf in coastal Alabama during spring." Continental Shelf Research 74:25-34.
• Pointer to the wiki page that documents the article: Page
• Expected submission date:

[Villamizar, Pai, and Harmon 2015]

• Authors and affiliations: Sandra Villamizar, Henry Pai and Thomas Harmon, University of California, Merced
• Keywords of research area: river ecohydrology
• Tentative title: Producing long-term series of whole-stream metabolism using readily available data.
• Short abstract: Continuous water quality and river discharge data that are readily available through government websites may be used to produce valuable information about key processes within a river ecosystem. In this paper I describe in detail the steps for acquisition and processing of river flow, dissolved oxygen, temperature, and specific conductance data that, combined with atmospheric data and physical properties of the river reach of interest, allow for the production of a long-term series of whole stream metabolism. This information is key in understanding the structure and function of an ecosystem such as the San Joaquin River in the Central Valley of California which has been increasingly degraded during the last 60 years due to intensive human intervention but now, since 2010, has been going through a restoration effort. The key advantage of this tool is that it uses readily available information to produce knowledge about a river ecosystem. This set of scripts, written in the R code, can be used immediately for any other river for which the key parameters (river flow, dissolved oxygen, temperature, and specific conductivity) are available. The scripts can also be modified by users to fit their particular site conditions.

• Challenge: Reproducibility; Dark Code; Document new software/applications. This set of scripts was written after the necessity of generating daily estimates of metabolic rates for long periods of time and at various sites within the San Joaquin River.
• Relationship to other publications: This will be a new publication
• Pointer to the wiki page that documents the article: Page
• Expected submission date: To be defined

Yu: [Yu, Bhatt, Rousseau, Pardo-Alvarez, and Duffy 2015]

• Authors and affiliations: Xuan Yu, Department of Geological Sciences, University of Delaware.
  

Gopal Bhatt, Department of Civil & Environmental Engineering, Pennsylvania State University.
Alain N. Rousseau, Institut National de la Recherche Scientifique (Centre Eau, Terre et Environnement), Université du Québec, 490 rue de la Couronne, Québec City, QC, Canada, G1K 9A9.
Alvaro Pardo-Alvarez, Institut National de la Recherche Scientifique (Centre Eau, Terre et Environnement), Université du Québec, 490 rue de la Couronne, Québec City, QC, Canada, G1K 9A9.
Chris Duffy, Department of Civil & Environmental Engineering, Pennsylvania State University.

• Keywords of research area: coupled processes, integrated hydrologic modeling, PIHM, surface flow, subsurface flow, open science
• Tentative title: Learning integrated modeling of coupled surface and subsurface hydrology from scratch
• Short abstract: Integrated modeling of coupled surface and subsurface flow has been of great interest in understanding not only intimate interconnectedness of hydrological processes, but also land-surface energy balance, biogeochemical and ecological processes, and landscape evolution. Although a growing number of complex hydrologic models have been used for resolving environmental processes, hypothesis testing, hydrologic predictions for effective management of watershed, very limited resources of the model provenance have been made accessible to a large group of potential model users. The users have to invest a significant amount of time and effort to reproduce, and understand the workflow of hydrologic simulation in a modeling paper. To provide a challenging and stimulating introduction to integrated modeling of coupled surface and subsurface flow, we revisit the development of PIHM (Penn State Integrated Hydrologic Model) by reproducing a numerical benchmarking example, and a real watershed application. Specifically, we document PIHM and its modeling workflow to enable basic understanding of simulating coupled surface and subsurface flow processes. We provide model and data to highlight the reciprocal roles between each other. In addition, we incorporate user experience as third dimension in the modeling workflow to enable clear provenance and deeper communications between model developers and users. The workflow has important implications for smoothing and accelerating open scientific collaborations in geosciences research.
• Challenge: Reproducibility; Reproduce published simulations by a existing model with the latest version. Benchmarking modeling application for numerical experiment and field data.
• Relationship to other publications: The article is based on a previously published article.
• Pointer to the wiki page that documents the article: Page
• Expected submission date: End of June 2015

Special Issue Editors

Chris Duffy, Scott Peckham, Cedric David, and Karan Venayagamoorthy.

The editors will only accept submissions that follow the special issue review criteria.

The editors will select a set of reviewers to handle the submissions. Reviewers will include experts in geosciences, computer science, and library sciences.

Special Issue Review Criteria

The reviewers will be asked to provide feedback on the papers according to the following criteria. Note that some papers will have good reasons for limiting the information (e.g. the data is from third parties and not openly available, etc), and in that case they would document those reasons.

• Documentation of the datasets: descriptions of datasets, unique identifiers, repositories.
• Documentation of software: description of all software used (including pre-processing of data, visualization steps, etc), unique identifiers, repositories.
• Documentation of the provenance of results: provenance for each figure or result, such as the workflow or the provenance record.

Submissions will be in two categories:

1. Technical papers: articles that have a novel contribution in some technical area of geosciences
2. Technical notes: articles that do not have a significant novel technical contribution, and whose major contribution is to reproduce a previously published result or illustrate the computational aspects of research published elsewhere

Tentative Timeline

• Journal committed to special issue: April 30, 2015
• Submissions due to editors: June 30, 2015
• Reviews due: Sept 15, 2015
• Decisions out to authors: Sept 30, 2015
• Revisions due: October 31, 2015
• Final versions due November 15, 2015
• Issue published December 31, 2015