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Dataset abstract The dataset includes annotated corpus data of N = 1811 utterances based on a picture description task that elicited semi-spontaneous oral production data from 15 Dutch learners of Hindi, from four (cross-sectional) stages (Years) of the Hindi course trajectory. The corpus data is annotated for (i) Learner, (ii) Year of study of the learner, (iii) the use of ne as an ergative marker, (iv) correct usage of the ne-marker, (v) the use of ko as a Differential Object Marker, (vi) the use of ko as another marker, and multiple features associated with ne- and ko-marking, including: (vii) specificity of the Direct Object, (viii) animacy of the Direct Object, (ix) transitivity of the sentence Verb, (x) perfectivity of the sentence Verb, (xi) other uses of the ko-marker, (xii) the semantic role of these other uses of the ko-marker. Article abstract We investigated the acquisition of Hindi split ergativity (ne-marking) and Differential Object Marking (zero or komarking) by L1 speakers of Dutch. Both grammatical phenomena are conditioned by multiple syntactic and semantic features. On a descriptive level, the study aims to examine when and how Dutch learners acquire and apply the conditional features associated with ne- and ko-marking. A specific learner corpus was created based on a picture description task that elicited semi-spontaneous oral production data from 15 Dutch learners of Hindi, from four (cross-sectional) stages of the Hindi course trajectory. We annotated the corpus data for multiple features associated with ne- and ko-marking. Using a mixed-effects logistic regression analysis, we found an increase in the use and accuracy of each case marker over the different years of study, but individual learner profile analyses revealed considerable intersubject differences in learner behaviour. We show that it is possible to define developmental stages for the acquisition of ne- and ko-marking in line with Processability Theory.

This release corresponds to the datasets generated from a thesis work that explores how information for protein functions can be exploited through embeddings so that the produced information can be used to improve protein function annotations. The underlying hypothesis here is that any pair of proteins with high sequence similarity will also share a similar biological function which would be reflected by the corresponding protein embeddings. The comparison and evaluation of this is done using two text-driven embedding approaches: Word2doc2Vec and Hybrid-Word2doc2Vec.

This is the corresponding data set for the article "Understanding levels of online participation in the UK museum sector". This data set uses a representative sample of 315 UK museums to create a much-needed benchmark against which museum practitioners can evaluate and contextualise prior studies and their own experiences. It includes data from museum websites and five social media platforms, and is one of the largest data sets of its kind in the European museum sector and the first of such scale in the UK. This work was supported by the Arts and Humanities Research Council under Grant AH/R012415/1.

ColBERTv2 Zeroshot Results for LoTTE dev set.

The image data set of various factions of Chinese ceramics, which is crawled and preprocessed from Baidu pictures, contains a total of 18,295 pictures and labels, including blue and white porcelain, pastel porcelain, Linglong porcelain, color glaze porcelain (four famous porcelain), other porcelain, purple pottery, purple clay pottery, laurel pottery, Anpottery (four famous pottery), other pottery, a total of 10 categories.The data set includes two parts: training set and test set. 10 pictures of class objects are stored in folders with corresponding numbers (0-9). Among them, the training set is divided into 10 chunks according to 10-fold cross-validation.

ACE2005 English dataset contains various types of data composed of entity, relationship, and event annotation, and supports English, Arabic, and Chinese. The dataset annotates 16,375 sentences in 599 documents. The data sources of the documents mainly come from the following six fields: Newswire, radio news, radio talks, blogs, newsgroups, and telephone conversations. The submitted data preprocessed ACE2005 data set, added dependency analysis results, and divided it into three data sets: training set, test set, and verification set.

Open Context (https://opencontext.org) publishes free and open access research data for archaeology and related disciplines. An open source (but bespoke) Django (Python) application supports these data publishing services. The software repository is here: https://github.com/ekansa/open-context-py The Open Context team runs ETL (extract, transform, load) workflows to import data contributed by researchers from various source relational databases and spreadsheets. Open Context uses PostgreSQL (https://www.postgresql.org) relational database to manage these imported data in a graph style schema. The Open Context Python application interacts with the PostgreSQL database via the Django Object-Relational-Model (ORM). In 2023, the Open Context team finished migration of from a legacy database schema to a revised and refactored database schema with stricter referential integrity and better consistency across tables. During this process, the Open Context team de-duplicated records, cleaned some metadata, and redacted attribute data left over from records that had been incompletely deleted in the legacy schema. This database dump includes all Open Context data organized with the legacy schema (table names that start with the 'oc_' or 'link_' prefixes) along with all Open Context data after cleanup and migration to the new database schema (table names that start with 'oc_all_'). The binary media files referenced by these structured data records are stored elsewhere. Binary media files for some projects, still in preparation, are not yet archived with long term digital repositories. These data comprehensively reflect the structured data currently published and publicly available on Open Context. Other data (such as user and group information) used to run the Website are not included. IMPORTANT This database dump contains data from roughly 180 different projects. Each project dataset has its own metadata and citation expectations. If you use these data, you must cite each data contributor appropriately, not just this Zenodo archived database dump. This database dump contains data from roughly 180 different projects. Each project dataset has its own metadata and citation expectations. If you use these data, you must cite each data contributor appropriately, not just this Zenodo archived database dump.

Harmony is a data harmonisation project that uses Natural Language Processing to help researchers make better use of existing data from different studies by supporting them with the harmonisation of various measures and items used in different studies. Harmony is a collaboration project between the University of Ulster, University College London, the Universidade Federal de Santa Maria in Brazil, and Fast Data Science Ltd. You can read more at https://harmonydata.org. There is a live demo at: https://app.harmonydata.org/ These are the datasets used to validate Harmony. The Excel file is McElroy et al's data, and the zip file contains the English and Portuguese GAD-7s.

The informal survey used to determine the parameter weights (feature importances) for the Clinical Trial Risk Tool (http://app.clinicaltrialrisk.org/). License: CC0 1.0 Universal Statement of Purpose The laws of most jurisdictions throughout the world automatically confer exclusive Copyright and Related Rights (defined below) upon the creator and subsequent owner(s) (each and all, an "owner") of an original work of authorship and/or a database (each, a "Work"). Certain owners wish to permanently relinquish those rights to a Work for the purpose of contributing to a commons of creative, cultural and scientific works ("Commons") that the public can reliably and without fear of later claims of infringement build upon, modify, incorporate in other works, reuse and redistribute as freely as possible in any form whatsoever and for any purposes, including without limitation commercial purposes. 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The Palace of Malia is situated at the eastern end of a fertile coastal plain, delimited on the north by the coastline of the Sea of Crete and on the south by the Selena mountain range. It is the third-largest Minoan palace (7500 m²) and lies around 45 km distant from the Palace of Knossos. The dataset contains detailed and accurate, photorealistic reconstructions of the remaining architectural structures from the Palace of Malia. Geographical information on the location of Malia can be found in the following link: https://www.geonames.org/257858/malia.html