Beyond advertising: New infrastructures for publishing integrated research objects

ABSTRACT: Moving beyond static text and illustrations is a central challenge for scientific publishing in the 21st century. As early as 1995, Donoho and Buckheit paraphrased John Claerbout that “an article about [a] computational result is advertising, not scholarship. The actual scholarship is the full software environment, code and data, that produced the result” [1]. Awareness of this problem has only grown over the last 25 years; nonetheless, scientific publishing infrastructures remain remarkably resistant to change [2]. Even as these infrastructures have largely stagnated, the internet has ushered in a transition “from the wet lab to the web lab” [3]. New expectations have emerged in this shift, but these expectations must play against the reality of currently available infrastructures and associated sociological pressures. Here, we compare current scientific publishing norms against those associated with online content more broadly, and we argue that meeting the “Claerbout challenge” of providing the full software environment, code, and data supporting a scientific result will require open infrastructure development to create environments for authoring, reviewing, and accessing interactive research objects

Conference of Microelectronics Research 2002

https://scholarworks.rit.edu/meec_archive/1011/thumbnail.jp

Compositional variation in grassland plant communities

Human activities are altering ecological communities around the globe. Understanding the implications of these changes requires that we consider the composition of those communities. However, composition can be summarized by many metrics which in turn are influenced by different ecological processes. For example, incidence-based metrics strongly reflect species gains or losses, while abundance-based metrics are minimally affected by changes in the abundance of small or uncommon species. Furthermore, metrics might be correlated with different predictors. We used a globally distributed experiment to examine variation in species composition within 60 grasslands on six continents. Each site had an identical experimental and sampling design: 24 plots × 4 years. We expressed compositional variation within each site—not across sites—using abundance- and incidence-based metrics of the magnitude of dissimilarity (Bray–Curtis and Sorensen, respectively), abundance- and incidence-based measures of the relative importance of replacement (balanced variation and species turnover, respectively), and species richness at two scales (per plot-year [alpha] and per site [gamma]). Average compositional variation among all plot-years at a site was high and similar to spatial variation among plots in the pretreatment year, but lower among years in untreated plots. For both types of metrics, most variation was due to replacement rather than nestedness. Differences among sites in overall within-site compositional variation were related to several predictors. Environmental heterogeneity (expressed as the CV of total aboveground plant biomass in unfertilized plots of the site) was an important predictor for most metrics. Biomass production was a predictor of species turnover and of alpha diversity but not of other metrics. Continentality (measured as annual temperature range) was a strong predictor of Sorensen dissimilarity. Metrics of compositional variation are moderately correlated: knowing the magnitude of dissimilarity at a site provides little insight into whether the variation is driven by replacement processes. Overall, our understanding of compositional variation at a site is enhanced by considering multiple metrics simultaneously. Monitoring programs that explicitly incorporate these implications, both when designing sampling strategies and analyzing data, will have a stronger ability to understand the compositional variation of systems and to quantify the impacts of human activities

The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.Development of the BIDS Standard has been supported by the International Neuroinformatics Coordinating Facility, Laura and John Arnold Foundation, National Institutes of Health (R24MH114705, R24MH117179, R01MH126699, R24MH117295, P41EB019936, ZIAMH002977, R01MH109682, RF1MH126700, R01EB020740), National Science Foundation (OAC-1760950, BCS-1734853, CRCNS-1429999, CRCNS-1912266), Novo Nordisk Fonden (NNF20OC0063277), French National Research Agency (ANR-19-DATA-0023, ANR 19-DATA-0021), Digital Europe TEF-Health (101100700), EU H2020 Virtual Brain Cloud (826421), Human Brain Project (SGA2 785907, SGA3 945539), European Research Council (Consolidator 683049), German Research Foundation (SFB 1436/425899996), SFB 1315/327654276, SFB 936/178316478, SFB-TRR 295/424778381), SPP Computational Connectomics (RI 2073/6-1, RI 2073/10-2, RI 2073/9-1), European Innovation Council PHRASE Horizon (101058240), Berlin Institute of Health & Foundation Charité, Johanna Quandt Excellence Initiative, ERAPerMed Pattern-Cog, and the Virtual Research Environment at the Charité Berlin – a node of EBRAINS Health Data Cloud.N

The past, present, and future of the Brain Imaging Data Structure (BIDS)

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS

PyBIDS: Python tools for BIDS datasets

Brain imaging researchers regularly work with large, heterogeneous, high-dimensional datasets. Historically, researchers have dealt with this complexity idiosyncratically, with every lab or individual implementing their own preprocessing and analysis procedures. The resulting lack of field-wide standards has severely limited reproducibility and data sharing and reuse.To address this problem, we and others recently introduced the Brain Imaging Data Standard (BIDS; (Gorgolewski et al., 2016)), a specification meant to standardize the process of representing brain imaging data. BIDS is deliberately designed with adoption in mind; it adheres to a user-focused philosophy that prioritizes common use cases and discourages complexity. By successfully encouraging a large and ever-growing subset of the community to adopt a common standard for naming and organizing files, BIDS has made it much easier for researchers to share, reuse, and process their data (Gorgolewski et al., 2017).The ability to efficiently develop high-quality spec-compliant applications itself depends to a large extent on the availability of good tooling. Because many operations recur widely across diverse contexts—for example, almost every tool designed to work with BIDS datasets involves regular file-filtering operations—there is a strong incentive to develop utility libraries that provide common functionality via a standardized, simple API.PyBIDS is a Python package that makes it easier to work with BIDS datasets. In principle, its scope includes virtually any functionality that is likely to be of general use when working with BIDS datasets (i.e., that is not specific to one narrow context). At present, its core and most widely used module supports simple and flexible querying and manipulation of BIDS datasets. PyBIDS makes it easy for researchers and developers working in Python to search for BIDS files by keywords and/or metadata; to consolidate and retrieve file-associated metadata spread out across multiple levels of a BIDS hierarchy; to construct BIDS-valid path names for new files; and to validate projects against the BIDS specification, among other applications.In addition to this core functionality, PyBIDS also contains an ever-growing set of modules that support additional capabilities meant to keep up with the evolution and expansion of the BIDS specification itself. Currently, PyBIDS includes tools for (1) reading and manipulating data contained in various BIDS-defined files (e.g., physiological recordings, event files, or participant-level variables); (2) constructing design matrices and contrasts that support the new BIDS-StatsModel specification (for machine-readable representation of fMRI statistical models); and (3) automated generation of partial Methods sections for inclusion in publications.PyBIDS can be easily installed on all platforms via pip (pip install pybids), though currently it is not officially supported on Windows. The package has few dependencies outside of standard Python numerical and image analysis libraries (i.e., numpy, scipy, pandas, and NiBabel). The core API is deliberately kept minimalistic: nearly all interactions with PyBIDS functionality occur through a core BIDSLayout object initialized by passing in a path to a BIDS dataset. For most applications, no custom configuration should be required.Although technically still in alpha release, PyBIDS is already being used both as a dependency in dozens of other open-source brain imaging packages –e.g., fMRIPrep (Esteban et al.,2019), MRIQC (Esteban et al., 2017), datalad-neuroimaging (https://github.com/datalad/datalad-neuroimaging), and fitlins (https://github.com/poldracklab/fitlins) – and directly in many researchers’ custom Python workflows. Development is extremely active, with bug fixes and new features continually being added (https://github.com/bids-standard/pybids), and major releases occurring approximately every 6 months. As of this writing, 29 people have contributed code to PyBIDS, and many more have provided feedback and testing. The API is relatively stable, and documentation and testing standards follow established norms for open-source scientific software. We encourage members of the brain imaging community currently working in Python to try using PyBIDS, and welcome new contributions

Compositional variation in grassland plant communities

Human activities are altering ecological communities around the globe. Understanding the implications of these changes requires that we consider the composition of those communities. However, composition can be summarized by many metrics which in turn are influenced by different ecological processes. For example, incidence-based metrics strongly reflect species gains or losses, while abundance-based metrics are minimally affected by changes in the abundance of small or uncommon species. Furthermore, metrics might be correlated with different predictors. We used a globally distributed experiment to examine variation in species composition within 60 grasslands on six continents. Each site had an identical experimental and sampling design: 24 plots × 4 years. We expressed compositional variation within each site—not across sites—using abundance- and incidence-based metrics of the magnitude of dissimilarity (Bray–Curtis and Sorensen, respectively), abundance- and incidence-based measures of the relative importance of replacement (balanced variation and species turnover, respectively), and species richness at two scales (per plot-year [alpha] and per site [gamma]). Average compositional variation among all plot-years at a site was high and similar to spatial variation among plots in the pretreatment year, but lower among years in untreated plots. For both types of metrics, most variation was due to replacement rather than nestedness. Differences among sites in overall within-site compositional variation were related to several predictors. Environmental heterogeneity (expressed as the CV of total aboveground plant biomass in unfertilized plots of the site) was an important predictor for most metrics. Biomass production was a predictor of species turnover and of alpha diversity but not of other metrics. Continentality (measured as annual temperature range) was a strong predictor of Sorensen dissimilarity. Metrics of compositional variation are moderately correlated: knowing the magnitude of dissimilarity at a site provides little insight into whether the variation is driven by replacement processes. Overall, our understanding of compositional variation at a site is enhanced by considering multiple metrics simultaneously. Monitoring programs that explicitly incorporate these implications, both when designing sampling strategies and analyzing data, will have a stronger ability to understand the compositional variation of systems and to quantify the impacts of human activities.Fil: Bakker, Jonathan. University of Washington; Estados UnidosFil: Price, Jodi N.. Charles Sturt University; AustraliaFil: Henning, Jeremiah A.. University of Minnesota; Estados Unidos. University of South Alabama; Estados UnidosFil: Batzer, Evan E.. University of California at Davis; Estados UnidosFil: Ohlert, Timothy. University of New Mexico; Estados UnidosFil: Wainwright, Claire E.. University of Washington; Estados UnidosFil: Adler, Peter. State University of Utah; Estados UnidosFil: Alberti, Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Arnillas, Carlos Alberto. University of Toronto; CanadáFil: Biederman, Lori A.. Iowa State University; Estados UnidosFil: Borer, Elizabeth. University of Minnesota; Estados UnidosFil: Brudvig, Lars A.. Michigan State University; Estados UnidosFil: Buckley, Yvonne M.. Trinity College Dublin; IrlandaFil: Bugalho, Miguel N.. Universidade de Lisboa; PortugalFil: Cadotte, Marc W.. University of Toronto; CanadáFil: Caldeira, Maria C.. Universidade de Lisboa; PortugalFil: Catford, Jane A.. Kings College London (kcl);Fil: Qingqing, Chen. Peking University; ChinaFil: Crawley, Michael J.. Imperial College. London Institute Of Medical Sciences.;Fil: Daleo, Pedro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; ArgentinaFil: Dickman, Chris R.. University of Sydney; AustraliaFil: Donohue, Ian. Trinity College Dublin; IrlandaFil: DuPre, Mary Ellyn. Mpg Ranch; Estados UnidosFil: Eisenhauer, Nico. German Centre For Integrative Biodiversity Research; Alemania. Universitat Leipzig; AlemaniaFil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria; ArgentinaFil: Roscher, Christiane. German Centre For Integrative Biodiversity Research; AlemaniaFil: Tedder, Michelle. University Of Kwazulu-natal; SudáfricaFil: Veen, G. F.. Netherlands Institute Of Ecology; Países BajosFil: Virtanen, Risto. University Of Oulu; FinlandiaFil: Wardle, Glenda M.. The University Of Sydney; Australi