CLICS: World’s largest database of cross-linguistic lexical associations

“Building on the new guidelines for standardized data formats in cross-linguistic research, which were first presented in 2018, the CLICS team was able to increase the amount of data from 300 language varieties and 1200 concepts in the original database to 3156 language varieties and 2906 concepts in the current installation. The new version also guarantees the reproducibility of the data aggregation process, conforming to best practices in research data management. “Thanks to the new standards and workflows we developed, our data is not only FAIR (findable, accessible, interoperable, and reproducible), but the process of lifting linguistic data from their original forms to our cross-linguistic standards is also much more efficient than in the past,” says Robert Forkel….”

The Beijing Declaration on Research Data

Grand challenges related to the environment, human health, and sustainability confront science and society. Understanding and mitigating these challenges in a rapidly changing environment require data[1] to be FAIR (Findable, Accessible, Interoperable, and Reusable) and as open as possible on a global basis. Scientific discovery must not be impeded unnecessarily by fragmented and closed systems, and the stewardship of research data should avoid defaulting to the traditional, proprietary approach of scholarly publishing. Therefore, the adoption of new policies and principles, coordinated and implemented globally, is necessary for research data and the associated infrastructures, tools, services, and practices. The time to act on the basis of solid policies for research data is now.

The Beijing Declaration on Research Data

Grand challenges related to the environment, human health, and sustainability confront science and society. Understanding and mitigating these challenges in a rapidly changing environment require data[1] to be FAIR (Findable, Accessible, Interoperable, and Reusable) and as open as possible on a global basis. Scientific discovery must not be impeded unnecessarily by fragmented and closed systems, and the stewardship of research data should avoid defaulting to the traditional, proprietary approach of scholarly publishing. Therefore, the adoption of new policies and principles, coordinated and implemented globally, is necessary for research data and the associated infrastructures, tools, services, and practices. The time to act on the basis of solid policies for research data is now.

TU Delft Strategic Plan Open Science 2020-2024 | TU Delft Repositories

Abstract:  Open Science is creating new forms of scientific interaction that were impossible or undreamed of in an earlier age. This has a strong impact on core academic processes like research, education and innovation. It is, for instance, easier to replicate an experiment if the relevant data sets are digitally available to any scientist who wishes to corroborate her colleague’s findings.TU Delft has a long history of engagement with Open Science. Yet, with its Open Science Programme 2020-2024, Research and Education in the Open Era, TU Delft wishes to take Open Science to the next level: a situation in which Open Science has become the default way of practising research and education, and the “information era” has become the “open era”. It is TU Delft’s ambition to be frontrunner in this revolutionary process. This is reflected in the TU Delft Strategic Framework 2018-2024, with “openness” as one of its major principles.The TU Delft Open Science Programme 2020-2024 tackles all areas of scholarly engagement where restrictions limit the flow of academic knowledge. It proposes new approaches to the process of research, education and innovation, with a strong focus on transparency, integrity and efficiency.The programme consists of five interrelated projects: Open Education, Open Access, Open Publishing Platform, FAIR Data, and FAIR Software. The projects are aimed at creating and disseminating various types of resources for the benefit of TU Delft researchers, teachers and students, as well as the general public. They will range from educational materials and software to a publishing platform. All outputs of the programme will be as ‘FAIR’ as possible: findable, accessible, interoperable and reusable.

Data Repository Selection: Criteria That Matter – Request For Comments – F1000 Blogs

“Publishers and journals are developing data policies to ensure that datasets, as well as other digital products associated with articles, are deposited and made accessible via appropriate repositories, also in line with the FAIR Principles. With thousands of options available, however, the lists of deposition repositories recommended by publishers are often different and consequently the guidance provided to authors may vary from journal to journal. This is due to a lack of common criteria used to select the data repositories, but also to the fact that there is still no consensus of what constitutes a good data repository. 

To tackle this, FAIRsharing and DataCite have joined forces with a group of publisher representatives (authors of this work) who are actively implementing data policies and recommending data repositories to researchers. The result of our work is a set of proposed criteria that journals and publishers believe are important for the identification and selection of data repositories, which can be recommended to researchers when they are preparing to publish the data underlying their findings. …”

Data Repository Selection: Criteria That Matter – Request For Comments – F1000 Blogs

“Publishers and journals are developing data policies to ensure that datasets, as well as other digital products associated with articles, are deposited and made accessible via appropriate repositories, also in line with the FAIR Principles. With thousands of options available, however, the lists of deposition repositories recommended by publishers are often different and consequently the guidance provided to authors may vary from journal to journal. This is due to a lack of common criteria used to select the data repositories, but also to the fact that there is still no consensus of what constitutes a good data repository. 

To tackle this, FAIRsharing and DataCite have joined forces with a group of publisher representatives (authors of this work) who are actively implementing data policies and recommending data repositories to researchers. The result of our work is a set of proposed criteria that journals and publishers believe are important for the identification and selection of data repositories, which can be recommended to researchers when they are preparing to publish the data underlying their findings. …”