What can the humanities do for data science? | The Alan Turing Institute

“The paper outlines recommendations in seven areas across two themes to support, and further, interdisciplinary research in data science and humanities, including:

Research Process 

Methodological frameworks and epistemic cultures: Develop common methodological frameworks/terminology and encourage wider use of shared research protocols in these areas. 
Best practices in the use and evaluation of computational tools: Use practices that ensure transparency and openness in research, and training programmes to help choose the most suitable computational tools in humanities research. 
Reproducible and open research: Promote transparent and reproducible research in the humanities, including data, code, workflows, computational environments, methods, and documentation. …”

Open access in silico tools to predict the ADMET profiling of drug candidates – PubMed

Abstract:  Introduction: We are in an era of bioinformatics and cheminformatics where we can predict data in the fields of medicine, the environment, engineering and public health. Approaches with open access in silico tools have revolutionized disease management due to early prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of the chemically designed and eco-friendly next-generation drugs.

Areas covered: This review meticulously encompasses the fundamental functions of open access in silico prediction tools (webservers and standalone software) and advocates their use in drug discovery research for the safety and reliability of any candidate-drug. This review also aims to help support new researchers in the field of drug design.

Expert opinion: The choice of in silico tools is critically important for drug discovery and the accuracy of ADMET prediction. The accuracy largely depends on the types of dataset, the algorithm used, the quality of the model, the available endpoints for prediction, and user requirement. The key is to use multiple in silico tools for predictions and comparing the results, followed by the identification of the most probable prediction.

48. On Planetary in 2020: curatorial activism and open sourcing in service of digital preservation – Fresh and New

“Perhaps the most experimental aspect of Planetary’s acquisition was the fact that the museum released the source code online with an open license, allowing anyone to copy the code and modify it and adapt their copy to suit their interests. The intention of open-sourcing the code was to open the door to passionate fans of Planetary so they could aid in its long-term preservation and maintenance….

The longer term value of open sourcing the code, rather than inheriting the default closed source model (which is the case with almost all software acquisitions into museum collections) also lies in clarity that it provides for future generations….

During the acquisition process of Planetary, substantial work was done with Smithsonian’s General Counsel and the developers formerly Bloom LLC, to enable the open sourcing which has overall benefits for future preservation activities. The generosity of the developers and their efforts to prepare the source for release cannot be underestimated. By defaulting to open source at the time of acquisition means that future presertvation or presentation activities, emulation or other efforts cannot be stymied in the future by more conservative legal counsel, curators, or conservators at the museum….”

OA Switchboard initiative: progress report July 2020 – OASPA

“Delivering the Minimum Viable Product (MVP)  for the OA Switchboard initiative is almost a reality. With August around the corner, there will be a solution available to streamline the neutral exchange of OA related publication-level information between funders, institutions and publishers. This offers the potential to provide a breakthrough in the transformation of the market to enable Open Access as the predominant model of publication.

The MVP has been shaped throughout the 2020 project, overseen by OASPA and the Steering Committee, in close collaboration with representatives of all stakeholder groups. To this end, numerous individual and group meetings have taken place, open to anybody who wants to contribute to this industry-wide intermediary solution, that aims to provide standards, infrastructure, and back office services. Every month we’ve provided an update or report to ensure full transparency and allow everybody to participate. It has been amazing to experience how so many people, with such a variety of interests and representing a wide range of stakeholders, selflessly have shared their time and expertise to collaborate towards essential open source OA infrastructure – for the greater good of progress in scholarly communication….”

Publishing computational research – a review of infrastructures for reproducible and transparent scholarly communication | Research Integrity and Peer Review | Full Text

Abstract:  Background

The trend toward open science increases the pressure on authors to provide access to the source code and data they used to compute the results reported in their scientific papers. Since sharing materials reproducibly is challenging, several projects have developed solutions to support the release of executable analyses alongside articles.

Methods

We reviewed 11 applications that can assist researchers in adhering to reproducibility principles. The applications were found through a literature search and interactions with the reproducible research community. An application was included in our analysis if it (i) was actively maintained at the time the data for this paper was collected, (ii) supports the publication of executable code and data, (iii) is connected to the scholarly publication process. By investigating the software documentation and published articles, we compared the applications across 19 criteria, such as deployment options and features that support authors in creating and readers in studying executable papers.

Results

From the 11 applications, eight allow publishers to self-host the system for free, whereas three provide paid services. Authors can submit an executable analysis using Jupyter Notebooks or R Markdown documents (10 applications support these formats). All approaches provide features to assist readers in studying the materials, e.g., one-click reproducible results or tools for manipulating the analysis parameters. Six applications allow for modifying materials after publication.

Conclusions

The applications support authors to publish reproducible research predominantly with literate programming. Concerning readers, most applications provide user interfaces to inspect and manipulate the computational analysis. The next step is to investigate the gaps identified in this review, such as the costs publishers have to expect when hosting an application, the consideration of sensitive data, and impacts on the review process.

CoVis – Discover reliable COVID-19 research

“For the development of therapeutics and vaccines for COVID-19, scientists depend on reliable research results. To support them, Open Knowledge Maps and ReFigure have launched CoVis: a curated knowledge map of seminal works on COVID-19 from eight critical areas of biomedical research. The knowledge map is constantly evolving thanks to the collective editing of subject-matter experts.

CoVis enables you to spend less time reviewing coronavirus literature and more time on your research….

Unless otherwise noted, all content on CoVis is licensed under a Creative Commons Attribution 4.0 International License. The CoVis database is made available under CC0 (Public Domain Dedication). Our software is open source and hosted on Github….”

CoVis – Discover reliable COVID-19 research

“For the development of therapeutics and vaccines for COVID-19, scientists depend on reliable research results. To support them, Open Knowledge Maps and ReFigure have launched CoVis: a curated knowledge map of seminal works on COVID-19 from eight critical areas of biomedical research. The knowledge map is constantly evolving thanks to the collective editing of subject-matter experts.

CoVis enables you to spend less time reviewing coronavirus literature and more time on your research….

Unless otherwise noted, all content on CoVis is licensed under a Creative Commons Attribution 4.0 International License. The CoVis database is made available under CC0 (Public Domain Dedication). Our software is open source and hosted on Github….”

CoVis: a new tool to discover reliable COVID-19 research | Labs | eLife

“For the development of therapeutics and vaccines for COVID-19, scientists depend on validated knowledge on the coronavirus. But finding reliable research results is often difficult: with over 20,000 papers published on the topic in the last six months, scientists spend a lot of valuable time finding, reading and reviewing the literature.

CoVis is a new tool that enables scientists to kick-start their COVID-19 research. In this EU-funded project, experts compile seminal research in a freely accessible database. The resulting data is fed into a knowledge map, providing a quick and intuitive overview of the collected research output. Often key findings are addressed and substantiated by multiple research sources. In such cases, data and images from different sources are compiled into a visual dashboard called a ReFigure, to help readers quickly understand the various facets of the research topic….

CoVis is an open infrastructure following the principles of open science, and can therefore be fully reused. Content on CoVis is licensed under CC BY 4.0. The CoVis database is made available under CC0. Our software is open source and hosted on GitHub under the MIT license….”

CoVis: a new tool to discover reliable COVID-19 research | Labs | eLife

“For the development of therapeutics and vaccines for COVID-19, scientists depend on validated knowledge on the coronavirus. But finding reliable research results is often difficult: with over 20,000 papers published on the topic in the last six months, scientists spend a lot of valuable time finding, reading and reviewing the literature.

CoVis is a new tool that enables scientists to kick-start their COVID-19 research. In this EU-funded project, experts compile seminal research in a freely accessible database. The resulting data is fed into a knowledge map, providing a quick and intuitive overview of the collected research output. Often key findings are addressed and substantiated by multiple research sources. In such cases, data and images from different sources are compiled into a visual dashboard called a ReFigure, to help readers quickly understand the various facets of the research topic….

CoVis is an open infrastructure following the principles of open science, and can therefore be fully reused. Content on CoVis is licensed under CC BY 4.0. The CoVis database is made available under CC0. Our software is open source and hosted on GitHub under the MIT license….”