Reproducible Research Research Articles

Repeat it without me: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge.

T1 mapping is a widely used quantitative MRI technique, but its tissue-specific values remain inconsistent across protocols, sites, and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well-established inversion-recovery T1 mapping technique, using acquisition details from a seminal T1 mapping paper on a standardized phantom and in human brains. The challenge used the acquisition protocol from Barral et al. (2010). Researchers collected T1 mapping data on the ISMRM/NIST phantom and/or in human brains. Data submission, pipeline development, and analysis were conducted using open-source platforms. Intersubmission and intrasubmission comparisons were performed. Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also developed: https://rrsg2020.dashboards.neurolibre.org. The T1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T1 variations in vivo.

A guide to successful management of collaborative partnerships in quantitative research: An illustration of the science of team science.

Data-intensive research continues to expand with the goal of improving healthcare delivery, clinical decision-making, and patient outcomes. Quantitative scientists, such as biostatisticians, epidemiologists, and informaticists, are tasked with turning data into health knowledge. In academic health centres, quantitative scientists are critical to the missions of biomedical discovery and improvement of health. Many academic health centres have developed centralized Quantitative Science Units which foster dual goals of professional development of quantitative scientists and producing high quality, reproducible domain research. Such units then develop teams of quantitative scientists who can collaborate with researchers. However, existing literature does not provide guidance on how such teams are formed or how to manage and sustain them. Leaders of Quantitative Science Units across six institutions formed a working group to examine common practices and tools that can serve as best practices for Quantitative Science Units that wish to achieve these dual goals through building long-term partnerships with researchers. The results of this working group are presented to provide tools and guidance for Quantitative Science Units challenged with developing, managing, and evaluating Quantitative Science Teams. This guidance aims to help Quantitative Science Units effectively participate in and enhance the research that is conducted throughout the academic health centre-shaping their resources to fit evolving research needs.

Assessing the accuracy of an infrared-converted drone camera with Orange-Cyan-NIR filter for vegetation and environmental monitoring

Drones equipped with cameras sensitive to near-infrared wavelengths are increasingly being used in environmental assessment studies and in agriculture. These cameras are sensitive to vegetation cover, extent of eutrophication in water bodies, and aspects of crops such as growth vigour, biomass and potential yield. Single-sensor (‘RGB’) cameras with modified spectral filters that allow for capturing near-infrared wavelengths offer a low-cost alternative to multi-sensor multispectral cameras or spectrometers. However, some studies point to lower measurement accuracies by such infrared converted sensors. So, to what extent can infrared converted cameras be used to quantify vegetation condition? This case study compared vegetation indices calculated from infrared converted camera imagery to those measured by a drone-borne multispectral camera and a handheld NDVI meter, as captured over soybean and potato fields. The study found that infrared converted camera derived NDVI was consistently lower over vegetation than NDVI measured from the multispectral and handheld sensors. Further, all considered indices of the infrared converted camera displayed relative underestimation at high index values, but over estimation at low index values. The study builds on previous case studies with similar results by further evaluating the reflectance patterns of the individual image bands to find possible reasons for the discrepancy in vegetation index measurements. There is good agreement between the near-infrared bands of the respective sensors (r=0.87), but the respective red bands have weak correlation (r=−0.03). We discuss possible reasons for the lower vegetation index measurements observed by the infrared converted camera, noting broad band sensitivities, and differing central wavelengths, which may have caused overestimated reflectance in the red band. All processing and analysis were executed with open-source software, and source code is made available to support reproducible research.

Variations in Using Diagnosis Codes for Defining Age-Related Macular Degeneration Cohorts

Data harmonization is vital for secondary electronic health record data analysis, especially when combining data from multiple sources. Currently, there is a gap in knowledge as to how studies identify cohorts of patients with age-related macular degeneration (AMD), a leading cause of blindness. We hypothesize that there is variation in using medical condition codes to define cohorts of AMD patients that can lead to either the under- or overrepresentation of such cohorts. This study identified articles studying AMD using the International Classification of Diseases (ICD-9, ICD-9-CM, ICD-10, and ICD-10-CM). The data elements reviewed included the year of publication; dataset origin (Veterans Affairs, registry, national or commercial claims database, and institutional EHR); total number of subjects; and ICD codes used. A total of thirty-seven articles were reviewed. Six (16%) articles used cohort definitions from two ICD terminologies. The Medicare database was the most used dataset (14, 38%), and there was a noted increase in the use of other datasets in the last few years. We identified substantial variation in the use of ICD codes for AMD. For the studies that used ICD-10 terminologies, 7 (out of 9, 78%) defined the AMD codes correctly, whereas, for the studies that used ICD-9 and 9-CM terminologies, only 2 (out of 30, 7%) defined and utilized the appropriate AMD codes (p = 0.0001). Of the 43 cohort definitions used from 37 articles, 31 (72%) had missing or incomplete AMD codes used, and only 9 (21%) used the exact codes. Additionally, 13 articles (35%) captured ICD codes that were not within the scope of AMD diagnosis. Efforts to standardize data are needed to provide a reproducible research output.

Cfr (v2024.1.26): a Python package for climate field reconstruction

Abstract. Climate field reconstruction (CFR) refers to the estimation of spatiotemporal climate fields (such as surface temperature) from a collection of pointwise paleoclimate proxy datasets. Such reconstructions can provide rich information on climate dynamics and provide an out-of-sample validation of climate models. However, most CFR workflows are complex and time-consuming, as they involve (i) preprocessing of the proxy records, climate model simulations, and instrumental observations; (ii) application of one or more statistical methods; and (iii) analysis and visualization of the reconstruction results. Historically, this process has lacked transparency and accessibility, limiting reproducibility and experimentation by non-specialists. This article presents an open-source and object-oriented Python package called cfr that aims to make CFR workflows easy to understand and conduct, saving climatologists from technical details and facilitating efficient and reproducible research. cfr provides user-friendly utilities for common CFR tasks such as proxy and climate data analysis and visualization, proxy system modeling, and modularized workflows for multiple reconstruction methods, enabling methodological intercomparisons within the same framework. The package is supported with extensive documentation of the application programming interface (API) and a growing number of tutorial notebooks illustrating its usage. As an example, we present two cfr-driven reconstruction experiments using the PAGES 2k temperature database applying the last millennium reanalysis (LMR) paleoclimate data assimilation (PDA) framework and the graphical expectation–maximization (GraphEM) algorithm, respectively.

Lost in Statistics

The present paper investigates one aspect of questionable research practices relating to Arabic L1 learners of foreign languages, namely the use of statistics. The objective of the paper is to argue that reproducible research requires adopting wise practices in linguistics and that the excessive focus on quantification does not seem to serve this purpose. Statistical significance tests in quantitative research are routinely used in linguistic inquiry as well as language teaching and learning studies with a view to supporting the relevant explanatory insights in linguistics. In this article, I will expose the misuse of statistics by doctoral students in English departments of Morocco working on Arabic L1 learners’ data, by highlighting some practices that are at odds with international good practices in academic research in linguistics. I will take stock of the current questionable practices in this regard to dispel some of the misunderstanding about the use of statistics which is now gaining grounds lest this becomes an orthodoxy. I will argue that research on Arabic L1 learners’ data should be focused more on exploration and discovery, as well as the validation of epistemological insights than on mere descriptive quantification geared to hypothesis verification. These areas of focus constitute the crux of academic research in linguistics, but they seem to be lost in statistics in doctoral students’ theses. Recommendations and solutions are provided for enhancing transparency and improving reproducibility of doctoral research outcomes to advance theory building and the delivery of new research lines in linguistics as well as to avoid the risk of research waste, in line with the requirements of open science.

The rise of Indo-German collaborative research: 1990–2022

Purpose This study aims to highlight the growth and development of Indo-German collaborative research over the past three decades. Moreover, this study encompasses an in-depth examination of funding acknowledgements to gain valuable insights into the financial support that underpins these collaborative endeavours. Together with this paper, the authors provide an openly accessible data set of Indo-German research papers for further and reproducible research activities (the “Indo-German Literature Dataset”). Design/methodology/approach The data were retrieved from the Web of Science (WoS) database from the year 1990 till the 30th of November 2022. A total of 36,999 records were retrieved against the used query. Acknowledged entities were extracted using a named entity recognition (NER) model specifically trained for this task. Interrelations between the extracted entities and scientific domains, lengths of acknowledgement texts, number of authors and affiliations, number of citations and gender of the first author, as well as collaboration patterns between Indian and German funders were examined. Findings The study reveals a consistent and increasing growth in the publication trend over the years. The study brings to light that Physics, Chemistry, Materials Science, Astronomy and Astrophysics and Engineering prominently dominate the Indo-German collaborative research. The USA, followed by England and France, are the most active collaborators in Indian and German research. Largely, research was funded by major German and Indian funding agencies, international corporations and German and American universities. Associations between the first author’s gender and acknowledged entity were observed. Additionally, relations between entity, entity type and scientific domain were discovered. Practical implications The study paves the way for enhanced collaboration, optimized resource utilization and societal advantages by offering a profound comprehension of the intricacies inherent in research partnerships between India and Germany. Implementation of the insights gleaned from this study holds the promise of cultivating a more resilient and influential collaborative research ecosystem between the two nations. Originality/value The study highlights a deeper understanding of the composition of the Indo-German collaborative research landscape of the past 30 years and its significance in advancing scientific knowledge and fostering international partnerships. Furthermore, the authors provide an open version of the original WoS data set. The Indo-German Literature Data set consists of 22,844 papers from OpenAlex and is available for related studies like literature studies and scientometrics.

Introduction to Reproducible Geospatial Analysis and Figures in R: A Tutorial Article

The present article is intended to serve an educational purpose for data scientists and students who already have experience with the R language and which to start using it for geospatial analysis and map creation. The basic concepts of raster data, vector data, CRS and datum are first presented along with a basic workflow to conduct reproducible geospatial research in R. Examples of important types of maps (scatter, bubble, choropleth, hexbin and faceted) created from open-source environmental data are illustrated and their practical implementation in R is discussed. Through these examples, essential manipulations on geospatial vector data are demonstrated (reading, transforming CRS, creating geometries from scratch, buffer zones around existing geometries and intersections between geometries).

A protocol for anti-CD4 IgG antibody purification using plasma samples from people with HIV and antibody-mediated cytotoxicity

Background. Up to 20% of people with HIV (PWH) fail to recover their CD4+ T cell counts to levels similar to healthy controls after suppressive antiretroviral therapy (ART). Immune non-responders (INRs) are PWH on suppressive ART with CD4+ T cell counts lower than 350 cells/mL, whereas their CD8+ T cell counts are higher than healthy controls. We are the first group to report that increased anti-CD4 autoantibody IgGs in INRs are responsible for blunted CD4+ T cell reconstitution in PWH with ART and viral suppression through anti-CD4 IgG-induced antibody-mediated cytotoxicity (ADCC) against CD4+ T cells in vitro. Notably, anti-CD4 IgG-mediated poor CD4+ T cell recovery from suppressive ART is the only mechanism targeting CD4+ T cells, specifically. Results. We provide a detailed one-by-one step protocol from antigen-specific antibody isolation using plasma samples, to ADCC assay. Conclusions. To promote reproducible research, a detailed protocol for isolating anti-CD4 IgG autoantibodies from plasma samples of PWH and evaluating ADCC effects is reported here.•Antigen-specific antibody isolation using human plasma samples•Antibody-mediated cytotoxicity (ADCC)

Reproducible machine learning research in mental workload classification using EEG.

This study addresses concerns about reproducibility in scientific research, focusing on the use of electroencephalography (EEG) and machine learning to estimate mental workload. We established guidelines for reproducible machine learning research using EEG and used these to assess the current state of reproducibility in mental workload modeling. We first started by summarizing the current state of reproducibility efforts in machine learning and in EEG. Next, we performed a systematic literature review on Scopus, Web of Science, ACM Digital Library, and Pubmed databases to find studies about reproducibility in mental workload prediction using EEG. All of this previous work was used to formulate guidelines, which we structured along the widely recognized Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. By using these guidelines, researchers can ensure transparency and comprehensiveness of their methodologies, therewith enhancing collaboration and knowledge-sharing within the scientific community, and enhancing the reliability, usability and significance of EEG and machine learning techniques in general. A second systematic literature review extracted machine learning studies that used EEG to estimate mental workload. We evaluated the reproducibility status of these studies using our guidelines. We highlight areas studied and overlooked and identify current challenges for reproducibility. Our main findings include limitations on reporting performance on unseen test data, open sharing of data and code, and reporting of resources essential for training and inference processes.

A guide to selecting high-performing antibodies for amyloid-beta precursor protein for use in Western Blot, immunoprecipitation and immunofluorescence.

The amyloid-beta precursor protein is a transmembrane protein expressed in many tissues and highly concentrated in the brain. The protein is of significant interest due to its involvement in the generation of amyloidogenic β-amyloid peptides, prone to plaque formation that is characteristic of Alzheimer's Disease. The scientific community would benefit from the availability of high-quality anti-amyloid-beta precursor protein antibodies to enhance reproducible research on this target. In this study, we characterized eleven amyloid-beta precursor protein commercial antibodies for Western blot, immunoprecipitation, and immunofluorescence using a standardized experimental protocol based on comparing read-outs in knockout cell lines and isogenic parental controls. These studies are part of a larger, collaborative initiative seeking to address antibody reproducibility issues by characterizing commercially available antibodies for human proteins and publishing the results openly as a resource for the scientific community. While use of antibodies and protocols vary between laboratories, we encourage readers to use this report as a guide to select the most appropriate antibodies for their specific needs.

A guide to selecting high-performing antibodies for RNA-binding protein TIA1 for use in Western Blot, immunoprecipitation and immunofluorescence.

A member of the RNA-binding protein family, T-cell intracellular antigen-1 (TIA1) regulates mRNA translation and splicing as well as cellular stress by promoting stress granule formation. Variants of the TIA1 gene have implications in neurogenerative disorders including amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Reproducible research on TIA1 would be enhanced with the availability of high-quality anti-TIA1 antibodies. In this study, we characterized twelve TIA1 commercial antibodies for Western Blot, immunoprecipitation, and immunofluorescence using a standardized experimental protocol based on comparing read-outs in knockout cell lines and isogenic parental controls. We identified many high-performing antibodies and encourage readers to use this report as a guide to select the most appropriate antibody for their specific needs.

Creating a next-generation phenotype library: the health data research UK Phenotype Library.

To enable reproducible research at scale by creating a platform that enables health data users to find, access, curate, and re-use electronic health record phenotyping algorithms. We undertook a structured approach to identifying requirements for a phenotype algorithm platform by engaging with key stakeholders. User experience analysis was used to inform the design, which we implemented as a web application featuring a novel metadata standard for defining phenotyping algorithms, access via Application Programming Interface (API), support for computable data flows, and version control. The application has creation and editing functionality, enabling researchers to submit phenotypes directly. We created and launched the Phenotype Library in October 2021. The platform currently hosts 1049 phenotype definitions defined against 40 health data sources and >200K terms across 16 medical ontologies. We present several case studies demonstrating its utility for supporting and enabling research: the library hosts curated phenotype collections for the BREATHE respiratory health research hub and the Adolescent Mental Health Data Platform, and it is supporting the development of an informatics tool to generate clinical evidence for clinical guideline development groups. This platform makes an impact by being open to all health data users and accepting all appropriate content, as well as implementing key features that have not been widely available, including managing structured metadata, access via an API, and support for computable phenotypes. We have created the first openly available, programmatically accessible resource enabling the global health research community to store and manage phenotyping algorithms. Removing barriers to describing, sharing, and computing phenotypes will help unleash the potential benefit of health data for patients and the public.

Development of Certified Reference Material Solutions for Phytochemicals in Ginger and Kava.

Dietary supplements derived from botanicals are commonly consumed and investigated in biomedical studies for their potential health benefits. Accurate identification and quantification of key chemical constituents from botanical ingredients is necessary for consistent product preparations and reproducible research results. Manufacturers need quantitative reference materials of the chemical constituents of interest to verify the content of ingredients and products. The rigor and reproducibility of biomedical research is enhanced through thorough characterization of the interventions used in mechanistic, clinical, and safety investigations. Quantitative reference materials enable reliable product quality assessments and reproducible research results. Solution-based certified reference material (CRM) mixes were developed as calibrants for phytochemicals in ginger and kava. The kava CRM contained yangonin, desmethoxyyangonin, dihydrokavain, DL-kavain, methysticin, dihydromethysticin, flavokawain A, flavokawain B, and flavokawain C. The ginger CRM contained 6-gingerol, 8-gingerol, 10-gingerol, 6-shogaol, 8-shogaol, and 10-shogaol. Each phytochemical was sourced as an isolated compound and assigned a purity factor by a mass balance approach accounting for residual impurities. The solution standard mixes were formulated by gravimetric addition of each phytochemical incorporating the purity factor and diluting with acetonitrile to the target concentrations of 500 µg/mL for the gingerols and shogaols, 250 µg/mL for the kavalactones, and 25 µg/mL for the flavokawains. The concentration accuracy of each component in the solution mixes was analytically verified by ultra high performance liquid chromatography with ultraviolet detection (UHPLC-UV) assay comparison to an independently prepared calibration solution. Each component in the ginger and kava CRMs were within 5 and 7% of the target concentrations, respectively. Homogeneous kava and ginger phytochemical solution mixes were produced with accurate constituent concentrations and demonstrated good stability over 2 years. These solution mixes were launched as commercially available CRMs. These mixes can be used as accurate concentration stock solutions to prepare calibrators and controls for botanical dietary supplement product testing and standardization.

Abstract 920: Insights from the National Cancer Institute’s request for information on existing data sharing processes for NIH-funded research

Abstract Data sharing plays a crucial role in accelerating biomedical research discoveries, promoting rigorous and reproducible research, enabling research data reuse, and fostering public trust. To facilitate data sharing, a coordinated, sustainable, and streamlined infrastructure is required to store, preserve, retrieve, and make data findable, accessible, interoperable, and reusable. Starting January 25, 2023, researchers funded by the National Institutes of Health (NIH) are required to comply with the NIH Data Management and Sharing Policy (https://sharing.nih.gov/data-management-and-sharing-policy/about-data-management-and-sharing-policies/data-management-and-sharing-policy-overview) and indicate how research data funded by the NIH will be managed and broadly shared. As institutions prepare to meet these requirements, the National Cancer Institute (NCI) is looking to understand the existing processes of data storage, preservation, and sharing including what infrastructure and services are available to facilitate data sharing. To carry out this goal, the NCI’s Office of Data Sharing (ODS), dedicated to promoting responsible and efficient sharing of cancer research data, issued a Request for Information (RFI) to understand the existing data sharing processes for NIH-Funded Research. The RFI was designed to collect information on services (e.g. guidance on data standards and file formats), technologies (e.g. storage, compression, retrieval, archival, analysis tools, workbenches), and processes (e.g. governance, communication, education, outreach) that promote data sharing. ODS utilized the RFI to gather input from stakeholders involved in facilitating data sharing. This included personnel from various fields such as research laboratories, scientific instrumentation core facilities, offices of research and/or sponsored programs, provost offices or other research administration, libraries, information technology, and institutional review boards. We also engaged with bioinformaticians and data scientists who are actively contributing to data curation, formatting, and analysis. Here, we present analysis of the responses received, including insights relating to gaps and challenges in data sharing, and proposed next steps and recommendations. Citation Format: Mousumi Ghosh, Nathan Boyd, Subhashini Jagu. Insights from the National Cancer Institute’s request for information on existing data sharing processes for NIH-funded research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 920.

Quality Management and Animal Welfare as Culture of Care for Animal Experiments under Biosafety Level-3, taking COVID-19 as a Practical Example

Abstract Since the start of the COVID‐19 pandemic, a tremendous amount of work has been done by scientists to understand the pathogenesis of the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Several animal models have been reported to be useful for the development and testing of vaccines, antivirals, or therapeutics. These in vivo experiments have to be performed at high containment biosafety level (BSL)-3. Thus, an appropriate biosafety, facility and data management as well as high standard of animal welfare, thorough study design, and staff training combined with an overall research transparency and open communication need to be implemented to achieve high quality reproducible research. Such an approach combined with the well-known 3R principle (Replacement, Reduction, and Refinement) and the 3S principle (Good Science, Good Sense, and Good Sensibilities) can contribute to culture of care in scientific research. The perspective reviews the key points of optimized biosafety, data, quality and animal welfare management in high risk biosafety-settings to conduct animal experiments with infectious pathogens in the context of a culture of care.

CREDO: a friendly Customizable, REproducible, DOcker file generator for bioinformatics applications

BackgroundThe analysis of large and complex biological datasets in bioinformatics poses a significant challenge to achieving reproducible research outcomes due to inconsistencies and the lack of standardization in the analysis process. These issues can lead to discrepancies in results, undermining the credibility and impact of bioinformatics research and creating mistrust in the scientific process. To address these challenges, open science practices such as sharing data, code, and methods have been encouraged.ResultsCREDO, a Customizable, REproducible, DOcker file generator for bioinformatics applications, has been developed as a tool to moderate reproducibility issues by building and distributing docker containers with embedded bioinformatics tools. CREDO simplifies the process of generating Docker images, facilitating reproducibility and efficient research in bioinformatics. The crucial step in generating a Docker image is creating the Dockerfile, which requires incorporating heterogeneous packages and environments such as Bioconductor and Conda. CREDO stores all required package information and dependencies in a Github-compatible format to enhance Docker image reproducibility, allowing easy image creation from scratch. The user-friendly GUI and CREDO's ability to generate modular Docker images make it an ideal tool for life scientists to efficiently create Docker images. Overall, CREDO is a valuable tool for addressing reproducibility issues in bioinformatics research and promoting open science practices.

Releasing synthetic data from the Avon Longitudinal Study of Parents and Children (ALSPAC): Guidelines and applied examples

The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective birth cohort. Since its inception in the early 1990s, the study has collected over thirty years of data on approximately 15,000 mothers, their partners, and their offspring, resulting in over 100,000 phenotype variables to date. Maintaining data security and participant anonymity and confidentiality are key principles for the study, meaning that data access is restricted to bona fide researchers who must apply to use data, which is then shared on a project-by-project basis. Despite these legitimate reasons for restricting data access, this does run counter to emerging best scientific practices encouraging making data openly available to facilitate transparent and reproducible research. Given the rich nature of the resource, ALSPAC data are also a valuable educational tool, used for teaching a variety of methods, such as longitudinal modelling and approaches to modelling missing data. To support these efforts and to overcome the restrictions in place with the study’s data sharing policy, we discuss methods for generating and making openly available synthesised ALSPAC datasets; these synthesised datasets are modelled on the original ALSPAC data, thus maintaining variable distributions and relations among variables (including missing data) as closely as possible, while at the same time preserving participant anonymity and confidentiality. We discuss how ALSPAC data can be synthesised using the ‘synthpop’ package in the R statistical programming language (including an applied example), present a list of guidelines for researchers wishing to release such synthesised ALSPAC data to follow, and demonstrate how this approach can be used as an educational tool to illustrate longitudinal modelling methods.

OpenEOcubes: an open-source and lightweight R-based RESTful web service for analyzing earth observation data cubes

In recent decades, Earth Observation (EO) systems have seen remarkable technological advancements, leading to a surge in Earth-orbiting satellites capturing EO data. Cloud-based storage solutions have been adopted to manage the increasing data volume. Although numerous EO data management and analysis platforms have emerged to accommodate this growth, many suffer from limitations like closed-source software, leading to platform lock-in and restricted functionalities, restricting the scientific community from conducting open and reproducible research. To tackle these issues, we present OpenEOcubes, a lightweight EO data cubes analysis service that embraces open-source tools, standardized APIs, and containerized deployment, we demonstrate the service’s capabilities in two user scenarios: performing vegetation analysis in Amazonia, Brazil for one year, and detecting changes in a forested area in Brandenburg, Germany based on five years of EO data.OpenEOcubes is an easy-to-deploy service that empowers the scientific community to reproduce small and medium-sized EO scientific analysis while aggregating over a potentially huge amount of data. It enables the extension of functionalities and validation of analysis carried out on different EO data processing platforms.

Publishing in the Open Access and Open Science era.

Our research activities would be better served if they were communicated in a manner that is openly accessible to the public and all researchers. The research we share is often limited to representative data included in research papers-science would be much more efficient if all reproducible research data were shared alongside detailed methods and protocols, in the paradigm called Open Science. On the other hand, one primary function of research journals is to select manuscripts of good quality, verify the authenticity of the data and its impact, and deliver to the appropriate audience for critical evaluation and verification. In the current paradigm, where publication in a subset of journals is intimately linked to research evaluation, a hypercompetitive "market" has emerged where authors compete to access a limited number of top-tier journals, leading to high rejection rates. Competition among publishers and scientific journals for market dominance resulted in an increase in both the number of journals and the cost of publishing and accessing scientific papers. Here we summarize the current problems and potential solutions from the development of AI technology discussed in the seminar at the 46th Annual Meeting of the Molecular Biology Society of Japan.