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Korea Social Science Data Archive (KOSSDA) has been integral in archiving and disseminating social science data in South Korea. As it transitioned into an independent research center under Seoul National University, we evaluated current data deposit process of KOSSDA through in-depth interviews with institutional data donors and identified future needs of data depositors. Motivations behind data deposit included recognizing data as a public good, addressing resource shortages in data curation, and enhancing data promotion. Future needs for KOSSDA included developing qualitative data guidelines, strengthening collaborations with donor institutions and academia as a whole, and automating the deposit process. These results guide the long-term strategy of KOSSDA to remain a leader in social science data archiving, ensuring its alignment with the evolving needs of the academic community. [ABSTRACT FROM AUTHOR]

Organised data is easy to use but the rapid developments in the field of bioimaging, with improvements in instrumentation, detectors, software and experimental techniques, have resulted in an explosion of the volumes of data being generated, making well-organised data an elusive goal. This guide offers a handful of recommendations for bioimage depositors, analysts and microscope and software developers, whose implementation would contribute towards better organised data in preparation for archival. Based on our experience archiving large image datasets in EMPIAR, the BioImage Archive and BioStudies, we propose a number of strategies that we believe would improve the usability (clarity, orderliness, learnability, navigability, self-documentation, coherence and consistency of identifiers, accessibility, succinctness) of future data depositions more useful to the bioimaging community (data authors and analysts, researchers, clinicians, funders, collaborators, industry partners, hardware/software producers, journals, archive developers as well as interested but non-specialist users of bioimaging data). The recommendations that may also find use in other data-intensive disciplines. To facilitate the process of analysing data organisation, we present bandbox, a Python package that provides users with an assessment of their data by flagging potential issues, such as redundant directories or invalid characters in file or folder names, that should be addressed before archival. We offer these recommendations as a starting point and hope to engender more substantial conversations across and between the various data-rich communities.

Genome data of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential for virus diagnosis, vaccine development, and variant surveillance. To archive and integrate worldwide SARS-CoV-2 genome data, a series of resources have been constructed, serving as a fundamental infrastructure for SARS-CoV-2 research, pandemic prevention and control, and coronavirus disease 2019 (COVID-19) therapy. Here we present an overview of extant SARS-CoV-2 resources that are devoted to genome data deposition and integration. We review deposition resources in data accessibility, metadata standardization, data curation and annotation; review integrative resources in data source, de-redundancy processing, data curation and quality assessment, and variant annotation. Moreover, we address issues that impede SARS-CoV-2 genome data integration, including low-complexity, inconsistency and absence of isolate name, sequence inconsistency, asynchronous update of genome data, and mismatched metadata. We finally provide insights into data standardization consensus and data submission guidelines, to promote SARS-CoV-2 genome data sharing and integration.

Organised data is easy to use but the rapid developments in the field of bioimaging, with improvements in instrumentation, detectors, software and experimental techniques, have resulted in an explosion of the volumes of data being generated, making well-organised data an elusive goal. This guide offers a handful of recommendations for bioimage depositors, analysts and microscope and software developers, whose implementation would contribute towards better organised data in preparation for archival. Based on our experience archiving large image datasets in EMPIAR, the BioImage Archive and BioStudies, we propose a number of strategies that we believe would improve the usability (clarity, orderliness, learnability, navigability, self-documentation, coherence and consistency of identifiers, accessibility, succinctness) of future data depositions more useful to the bioimaging community (data authors and analysts, researchers, clinicians, funders, collaborators, industry partners, hardware/software producers, journals, archive developers as well as interested but non-specialist users of bioimaging data). The recommendations that may also find use in other data-intensive disciplines. To facilitate the process of analysing data organisation, we present bandbox, a Python package that provides users with an assessment of their data by flagging potential issues, such as redundant directories or invalid characters in file or folder names, that should be addressed before archival. We offer these recommendations as a starting point and hope to engender more substantial conversations across and between the various data-rich communities.

Cryoelectron microscopy (cryo-EM) methods have made meaningful contributions in a wide variety of scientific research fields. In structural biology, cryo-EM routinely elucidates molecular structure from isolated biological macromolecular complexes or in a cellular context by harnessing the high-resolution power of the electron in order to image samples in a frozen, hydrated environment. For structural chemistry, the cryo-EM method popularly known as microcrystal electron diffraction (MicroED) has facilitated atomic structure generation of peptides and small molecules from their three-dimensional crystal forms. As cryo-EM has grown from an emerging technology, it has undergone modernization to enable multimodal transmission electron microscopy (TEM) techniques becoming more routine, reproducible, and accessible to accelerate research across multiple disciplines. We review recent advances in modern cryo-EM and assess how they are contributing to the future of the field with an eye to the past. [Display omitted] In this review, de la Cruz and Eng discuss major developments in single-particle analysis, electron tomography, and microcrystal electron diffraction to show how cryo-EM has matured to become an ever-improving technology for visualizing molecular structure. [ABSTRACT FROM AUTHOR]

Organised data is easy to use but the growth of bioimaging, with improvements in instrumentation, detectors, software and experimental techniques has resulted in an explosion in the volumes of data being generated, making this an elusive goal. This guide offers a handful of recommendations whose implementation would contribute towards better organised data in preparation for archival. Based on our experience archiving large image datasets in EMPIAR, the BioImage Archive and BioStudies, we propose a number of strategies that we believe would make future data depositions more useful to the bioimaging community and that may also find use in other data-intensive disciplines. To facilitate the process of analysing data organisation, we present bandbox, a Python package that provides users with an assessment of their data by flagging potential issues that could be addressed before archival.

Various next generation sequencing (NGS) based strategies have been successfully used in the recent past for tracing origins and understanding the evolution of infectious agents, investigating the spread and transmission chains of outbreaks, as well as facilitating the development of effective and rapid molecular diagnostic tests and contributing to the hunt for treatments and vaccines. The ongoing COVID-19 pandemic poses one of the greatest global threats in modern history and has already caused severe social and economic costs. The development of efficient and rapid sequencing methods to reconstruct the genomic sequence of SARS-CoV-2, the etiological agent of COVID-19, has been fundamental for the design of diagnostic molecular tests and to devise effective measures and strategies to mitigate the diffusion of the pandemic. Diverse approaches and sequencing methods can, as testified by the number of available sequences, be applied to SARS-CoV-2 genomes. However, each technology and sequencing approach has its own advantages and limitations. In the current review, we will provide a brief, but hopefully comprehensive, account of currently available platforms and methodological approaches for the sequencing of SARS-CoV-2 genomes. We also present an outline of current repositories and databases that provide access to SARS-CoV-2 genomic data and associated metadata. Finally, we offer general advice and guidelines for the appropriate sharing and deposition of SARS-CoV-2 data and metadata, and suggest that more efficient and standardized integration of current and future SARS-CoV-2-related data would greatly facilitate the struggle against this new pathogen. We hope that our ' vademecum ' for the production and handling of SARS-CoV-2-related sequencing data, will contribute to this objective. [ABSTRACT FROM AUTHOR]

Discovery and development of 210 new molecular entities (NMEs; new drugs) approved by the US Food and Drug Administration 2010–2016 was facilitated by 3D structural information generated by structural biologists worldwide and distributed on an open-access basis by the PDB. The molecular targets for 94% of these NMEs are known. The PDB archive contains 5,914 structures containing one of the known targets and/or a new drug, providing structural coverage for 88% of the recently approved NMEs across all therapeutic areas. More than half of the 5,914 structures were published and made available by the PDB at no charge, with no restrictions on usage >10 years before drug approval. Citation analyses revealed that these 5,914 PDB structures significantly affected the very large body of publicly funded research reported in publications on the NME targets that motivated biopharmaceutical company investment in discovery and development programs that produced the NMEs. Discovery/development of 210 new molecular entities (NMEs; new drugs) approved by the US Food and Drug Administration 2010–2016 was facilitated by open access to 3D structures stored in the PDB. Nearly 6,000 relevant PDB structures contributed to approval of 88% of these NMEs across all therapeutic areas. [ABSTRACT FROM AUTHOR]

The Metabolomics Workbench (MW) is a public scientific data repository consisting of experimental data and metadata from metabolomics studies collected with mass spectroscopy (MS) and nuclear magnetic resonance (NMR) analyses. MW has been constantly evolving; updating its ‘mwTab’ text file format, adding a JavaScript Object Notation (JSON) file format, implementing a REpresentational State Transfer (REST) interface, and nearly quadrupling the number of datasets hosted on the repository within the last three years. In order to keep up with the quickly evolving state of the MW repository, the ‘mwtab’ Python library and package have been continuously updated to mirror the changes in the ‘mwTab’ and JSONized formats and contain many new enhancements including methods for interacting with the MW REST interface, enhanced format validation features, and advanced features for parsing and searching for specific metabolite data and metadata. We used the enhanced format validation features to evaluate all available datasets in MW to facilitate improved curation and FAIRness of the repository. The ‘mwtab’ Python package is now officially released as version 1.0.1 and is freely available on GitHub and the Python Package Index (PyPI) under a Clear Berkeley Software Distribution (BSD) license with documentation available on ReadTheDocs.

Structural and functional studies require the development of sophisticated `Big Data' technologies and software to increase the knowledge derived and ensure reproducibility of the data. This paper presents summaries of the Structural Biology Knowledge Base, the VIPERdb Virus Structure Database, evaluation of homology modeling by the Protein Model Portal, the ProSMART tool for conformation-independent structure comparison, the LabDB `super' laboratory information management system and the Cambridge Structural Database. These techniques and technologies represent important tools for the transformation of crystallographic data into knowledge and information, in an effort to address the problem of non-reproducibility of experimental results.

The Protein Data Bank (PDB) is one of two archival resources for experimental data central to biomedical research and education worldwide (the other key Primary Data Archive in biology being the International Nucleotide Sequence Database Collaboration). The PDB currently houses >134,000 atomic level biomolecular structures determined by crystallography, NMR spectroscopy, and 3D electron microscopy. It was established in 1971 as the first open-access, digital-data resource in biology, and is managed by the Worldwide Protein Data Bank partnership (wwPDB; wwpdb.org). US PDB operations are conducted by the RCSB Protein Data Bank (RCSB PDB; RCSB.org; Rutgers University and UC San Diego) and funded by NSF, NIH, and DoE. The RCSB PDB serves as the global Archive Keeper for the wwPDB. During calendar 2016, >591 million structure data files were downloaded from the PDB by Data Consumers working in every sovereign nation recognized by the United Nations. During this same period, the RCSB PDB processed >5300 new atomic level biomolecular structures plus experimental data and metadata coming into the archive from Data Depositors working in the Americas and Oceania. In addition, RCSB PDB served >1 million RCSB.org users worldwide with PDB data integrated with ∼40 external data resources providing rich structural views of fundamental biology, biomedicine, and energy sciences, and >600,000 PDB101.rcsb.org educational website users around the globe. RCSB PDB resources are described in detail together with metrics documenting the impact of access to PDB data on basic and applied research, clinical medicine, education, and the economy. [ABSTRACT FROM AUTHOR]

Summary The Worldwide PDB recently launched a deposition, biocuration, and validation tool: OneDep. At various stages of OneDep data processing, validation reports for three-dimensional structures of biological macromolecules are produced. These reports are based on recommendations of expert task forces representing crystallography, nuclear magnetic resonance, and cryoelectron microscopy communities. The reports provide useful metrics with which depositors can evaluate the quality of the experimental data, the structural model, and the fit between them. The validation module is also available as a stand-alone web server and as a programmatically accessible web service. A growing number of journals require the official wwPDB validation reports (produced at biocuration) to accompany manuscripts describing macromolecular structures. Upon public release of the structure, the validation report becomes part of the public PDB archive. Geometric quality scores for proteins in the PDB archive have improved over the past decade. [ABSTRACT FROM AUTHOR]

Phylogenetic trees are a crucial backbone for a wide breadth of biological research spanning systematics, organismal biology, ecology, and medicine. In 2015, the Open Tree of Life project published a first draft of a comprehensive tree of life, summarizing digitally available taxonomic and phylogenetic knowledge. This paper reviews, investigates, and addresses the following questions as a follow-up to that paper, from the perspective of researchers involved in building this summary of the tree of life: Is there a tree of life and should we reconstruct it? Is available data sufficient to reconstruct the tree of life? Do we have access to phylogenetic inferences in usable form? Can we combine different phylogenetic estimates across the tree of life? And finally, what is the future of understanding the tree of life? [ABSTRACT FROM AUTHOR]

Summary OneDep, a unified system for deposition, biocuration, and validation of experimentally determined structures of biological macromolecules to the PDB archive, has been developed as a global collaboration by the worldwide PDB (wwPDB) partners. This new system was designed to ensure that the wwPDB could meet the evolving archiving requirements of the scientific community over the coming decades. OneDep unifies deposition, biocuration, and validation pipelines across all wwPDB, EMDB, and BMRB deposition sites with improved focus on data quality and completeness in these archives, while supporting growth in the number of depositions and increases in their average size and complexity. In this paper, we describe the design, functional operation, and supporting infrastructure of the OneDep system, and provide initial performance assessments. [ABSTRACT FROM AUTHOR]

The IUCr Diffraction Data Deposition Working Group is investigating the rationale and policies for routine deposition of diffraction images (and other primary experimental data sets). An information-management framework is described that should inform policy directions, and some of the technical and other issues that need to be addressed in an effort to achieve such a goal are analysed. In the near future, routine data deposition could be encouraged at one of the growing number of institutional repositories that accept data sets or at a generic data-publishing web repository service. To realise all of the potential benefits of depositing diffraction data, specialized archives would be preferable. Funding such an initiative will be challenging. [ABSTRACT FROM AUTHOR]

The Metabolomics Workbench (MW) is a public scientific data repository consisting of experimental data and metadata from metabolomics studies collected with mass spectroscopy (MS) and nuclear magnetic resonance (NMR) analyses. MW has been constantly evolving; updating its 'mwTab' text file format, adding a JavaScript Object Notation (JSON) file format, implementing a REpresentational State Transfer (REST) interface, and nearly quadrupling the number of datasets hosted on the repository within the last three years. In order to keep up with the quickly evolving state of the MW repository, the 'mwtab' Python library and package have been continuously updated to mirror the changes in the 'mwTab' and JSONized formats and contain many new enhancements including methods for interacting with the MW REST interface, enhanced format validation features, and advanced features for parsing and searching for specific metabolite data and metadata. We used the enhanced format validation features to evaluate all available datasets in MW to facilitate improved curation and FAIRness of the repository. The 'mwtab' Python package is now officially released as version 1.0.1 and is freely available on GitHub and the Python Package Index (PyPI) under a Clear Berkeley Software Distribution (BSD) license with documentation available on ReadTheDocs. [ABSTRACT FROM AUTHOR]

Few would have thought that when Porter and colleagues used light microscopy to target their cell of interest to be analyzed in the electron microscope in the 1940s, that Correlative Imaging would develop into the thriving field it is today. Even though the first use of Correlative Light Electron Microscopy (CLEM) was established in the 1940s, it is only since the year 2000 that there has been a real surge in the application of CLEM technology. The power of CLEM is recognized in the scientific community as evidenced by the growing number of publications and dedicated sessions at scientific meetings. The field is also broadening, incorporating a multitude of other techniques including preclinical research and diagnostics, and slowly but surely the overarching field of Correlative Multimodality Imaging (CMI) is taking its place as an established technique and a research area in its own right. In this chapter, we will look at the initiatives that are being developed within the scientific world to build a coherent CMI community, with a particular emphasis on the developments in Europe. To achieve this aim, the community will need to design mechanisms for the interdisciplinary exchange of knowledge and benefits, set up training schemes, and develop standards for CMI technology and its data., (Copyright © 2021 Elsevier Inc. All rights reserved.)

This article describes some of the activities of the IUCr Diffraction Data Deposition Working Group and introduces a collection of articles discussing the archiving of diffraction images. [ABSTRACT FROM AUTHOR]

The Protein Data Bank (PDB) is one of two archival resources for experimental data central to biomedical research and education worldwide (the other key Primary Data Archive in biology being the International Nucleotide Sequence Database Collaboration). The PDB currently houses >134,000 atomic level biomolecular structures determined by crystallography, NMR spectroscopy, and 3D electron microscopy. It was established in 1971 as the first open-access, digital-data resource in biology, and is managed by the Worldwide Protein Data Bank partnership (wwPDB; wwpdb.org). US PDB operations are conducted by the RCSB Protein Data Bank (RCSB PDB; RCSB.org; Rutgers University and UC San Diego) and funded by NSF, NIH, and DoE. The RCSB PDB serves as the global Archive Keeper for the wwPDB. During calendar 2016, >591 million structure data files were downloaded from the PDB by Data Consumers working in every sovereign nation recognized by the United Nations. During this same period, the RCSB PDB processed >5300 new atomic level biomolecular structures plus experimental data and metadata coming into the archive from Data Depositors working in the Americas and Oceania. In addition, RCSB PDB served >1 million RCSB.org users worldwide with PDB data integrated with ∼40 external data resources providing rich structural views of fundamental biology, biomedicine, and energy sciences, and >600,000 PDB101.rcsb.org educational website users around the globe. RCSB PDB resources are described in detail together with metrics documenting the impact of access to PDB data on basic and applied research, clinical medicine, education, and the economy., (© 2017 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.)