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Found 19 result(s)
The figshare service for the University of Sheffield allows researchers to store, share and publish research data. It helps the research data to be accessible by storing Metadata alongside datasets. Additionally, every uploaded item receives a Digital Object identifier (DOI), which allows the data to be citable and sustainable. If there are any ethical or copyright concerns about publishing a certain dataset, it is possible to publish the metadata associated with the dataset to help discoverability while sharing the data itself via a private channel through manual approval.
UCLA Library is adopting Dataverse, the open source web application designed for sharing, preserving and using research data. UCLA Dataverse will allow data, text, software, scripts, data visualizations, etc., created from research projects at UCLA to be made publicly available, widely discoverable, linkable, and ultimately, reusable
The Portal aims to serve as a unique access point to timely, comprehensive migration statistics and reliable information about migration data globally. The site is designed to help policy makers, national statistics officers, journalists and the general public interested in the field of migration to navigate the increasingly complex landscape of international migration data, currently scattered across different organisations and agencies. Especially in critical times, such as those faced today, it is essential to ensure that responses to migration are based on sound facts and accurate analysis. By making the evidence about migration issues accessible and easy to understand, the Portal aims to contribute to a more informed public debate. The Portal was launched in December 2017 and is managed and developed by IOM’s Global Migration Data Analysis Centre (GMDAC), with the guidance of its Advisory Board, and was supported in its conception by the Economist Intelligence Unit (EIU). The Portal is supported financially by the Governments of Germany, the United States of America and the UK Department for International Development (DFID).
University of Alberta Dataverse is a service provided by the University of Albert Library to help researchers publish, analyze, distribute, and preserve data and datasets. Open for University of Alberta-affiliated researchers to deposit data.
The Polinsky Language Sciences Lab at Harvard University is a linguistics lab that examines questions of language structure and its effect on the ways in which people use and process language in real time. We engage in linguistic and interdisciplinary research projects ourselves; offer linguistic research capabilities for undergraduate and graduate students, faculty, and visitors; and build relationships with the linguistic communities in which we do our research. We are interested in a broad range of issues pertaining to syntax, interfaces, and cross-linguistic variation. We place a particular emphasis on novel experimental evidence that facilitates the construction of linguistic theory. We have a strong cross-linguistic focus, drawing upon English, Russian, Chinese, Korean, Mayan languages, Basque, Austronesian languages, languages of the Caucasus, and others. We believe that challenging existing theories with data from as broad a range of languages as possible is a crucial component of the successful development of linguistic theory. We investigate both fluent speakers and heritage speakers—those who grew up hearing or speaking a particular language but who are now more fluent in a different, societally dominant language. Heritage languages, a novel field of linguistic inquiry, are important because they provide new insights into processes of linguistic development and attrition in general, thus increasing our understanding of the human capacity to maintain and acquire language. Understanding language use and processing in real time and how children acquire language helps us improve language study and pedagogy, which in turn improves communication across the globe. Although our lab does not specialize in language acquisition, we have conducted some studies of acquisition of lesser-studied languages and heritage languages, with the purpose of comparing heritage speakers to adults.
Additionally to the institutional repository, current St. Edward's faculty have the option of uploading their work directly to their own SEU accounts on stedwards.figshare.com. Projects created on Figshare will automatically be published on this website as well. For more information, please see documentation
The Comparative Agendas Project (CAP) assembles and codes information on the policy processes of governments from around the world. CAP enables scholars, students, policy-makers and the media to investigate trends in policy-making across time and between countries. It classifies policy activities into a single, universal and consistent coding scheme.
Provided by the University Libraries, KiltHub is the comprehensive institutional repository and research collaboration platform for research data and scholarly outputs produced by members of Carnegie Mellon University and their collaborators. KiltHub collects, preserves, and provides stable, long-term global open access to a wide range of research data and scholarly outputs created by faculty, staff, and student members of Carnegie Mellon University in the course of their research and teaching.
The Language Archive at the Max Planck Institute in Nijmegen provides a unique record of how people around the world use language in everyday life. It focuses on collecting spoken and signed language materials in audio and video form along with transcriptions, analyses, annotations and other types of relevant material (e.g. photos, accompanying notes).
Mulce (MUltimodal contextualized Learner Corpus Exchange) is a research project supported by the National Research Agency (ANR programme: "Corpus and Tools in the Humanities", ANR-06-CORP-006). A teaching corpus (LETEC - Learning and Teaching Corpora) combines a systematic and structured data set, particularly of interactional data, and traces left by a training course experimentation, conducted partially or completely online and completed by additional technical, human, pedagogical and scientific information to enable the data to be analysed in context.
Arca Data is Fiocruz's official repository for archiving, publishing, disseminating, preserving and sharing digital research data produced by the Fiocruz community or in partnership with other research institutes or bodies, with the aim of promoting new research, ensuring the reproducibility or replicability of existing research and promoting an Open and Citizen Science. Its objective is to stimulate the wide circulation of scientific knowledge, strengthening the institutional commitment to Open Science and free access to health information, in addition to providing transparency and fostering collaboration between researchers, educators, academics, managers and graduate students, to the advancement of knowledge and the creation of solutions that meet the demands of society.
Additional to the the e-publishing offer for articles, books and journals, Propylaeum provides classical scholars with the opportunity to archive the respective research data permanently. These can be linked directly to online publications hosted on the Heidelberg publishing platforms. All research data – e.g. images, videos, audio files, tables, graphics etc. – receive a DOI (Digital Object Identifiyer). Thus, they can be cited, viewed and permanently linked to as distinct academic output.
e-cienciaDatos is a multidisciplinary data repository that houses the scientific datasets of researchers from the public universities of the Community of Madrid and the UNED, members of the Consorcio Madroño, in order to give visibility to these data, to ensure its preservation And facilitate their access and reuse. e-cienciaDatos is structured as a system constituted by different communities that collects datasets of each of the individual universities. e-cienciaDatos offers the deposit and publication of datasets, assigning a digital object identifier DOI to each of them. The association of a dataset with a DOI will facilitate data verification, dissemination, reuse, impact and long-term access. In addition, the repository provides a standardized citation for each dataset, which contains sufficient information so that it can be identified and located, including the DOI.