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The dataset consists of a full morphosyntactic annotation of the normalized letter corpus of the State Archives of Assyria online (SAAo), plus associated metadata regarding sender, recipient, estimated date of composition, script, and dialect of Akkadian (if determinable). This corpus comprises ten of the twenty-one current volumes of SAAo and contains approximately 2600 letters from the royal archives of the late Neo-Assyrian kings. Each letter features morphosyntactic annotations specifying part of speech, lemma, morphological decomposition, and syntactic dependencies of all relevant tokens in the text. The annotations were made with the help of a spaCy language model with additional human checking and completion. The annotations are available both as a set of CONLLU files (one per text) and as linked open data in a single TTL file. The associated metadata is available as a CSV file and a TTL. Due to the letters' shared format, topics of concern, and historical period in which they were written, this corpus forms a natural object of study from a linguistic and social historical perspective. It is hoped this data will be of use to researchers wishing to do linguistic and sociolinguistic corpus research on these texts. 

This dataset contains the extended version of the authors' earlier work: https://zenodo.org/records/6507872, where pairs of news articles drawn from the first half of 2020 are annotated for seven aspects of similarity in the original version as well as an additional FRAME aspect: GEO: How similar is the geographic focus (places, cities, countries, etc.) of the two articles? ENT: How similar are the named entities (e.g., people, companies, organizations, products, named living beings), excluding previously considered locations appearing in the two articles? TIME Are the two articles relevant to similar time periods or describing similar time periods? NAR How similar are the narrative schemas presented in the two articles? OVERALL Overall, are the two articles covering the same substantive news story? (excluding style, framing, and tone) STYLE Do the articles have similar writing styles? TONE Do the articles have similar tones? FRAME Do the articles have similar framing and express similar opinions?

This dataset contains TEI files of Emily Dickinson poems. For more information on the files, see the Emily Dickinson Archive website https://www.edickinson.org/

We introduce the new comprehensive Open Review–Based dataset (ORB); it includes a curated list of more than 10,000 scientific papers with their more than 31,000 reviews and final decisions. We gather this information from two sources: the OpenReview.net and SciPost.org websites

This dataset supports the research paper, ""Modular Bibliographical Profiling of Historic Book Reviews." The paper examines different methods of predicting bibliographical details (e.g. author, title, and publisher) of books under review in a corpus of approximately 1,100 historical book reviews. The dataset is comprised of book reviews from ProQuest's American Periodicals Series (APS). This kind of bibliographical profiling is often characterized as a Natural Language Processing (NLP) or Named Entity Recognition (NER) task, but it can be more specifically described as a two-part Named Entity Linking (NEL) task, beginning with a feature extraction stage followed by one of several available matching or classification methods. An attempt has been made to formalize constraints for modular bibliographical profiling (MBP) and shed light on some important choices that are often glossed over or obscured by digital humanities (DH) practitioners. Applying these constraints, the paper evaluates combinations of feature selection (naive bag-of-words [BOW], rule-based feature extraction, and NER using a pre-trained model) with a standardized similarity-based matching strategy (cosine similarity). All tasks are performed on derived text data (term frequency tables), so that data can be shared and all methods can be used on materials available only in non-consumptive formats. These comparisons suggest that naive BOW can perform quite robustly, and that using even a basic pretrained NER model in conjunction with a BOW approach may reduce false positives. 

Pleiades Datasets 3.2 (3 November 2023) This is a package of data derived from the Pleiades gazetteer of ancient places. It is used for archival and redistribution purposes and is likely to be less up-to-date than the live data at https://pleiades.stoa.org. We encourage use and citation of these numbered releases for scholarly work that will be published in static form. Updates and additions to published content of the Pleiades gazetteer of ancient places between 1 August 2023 and 3 November 2023 What's new since 3.1 (1 August 2023): 108 new and 1,629 updated place resources reflecting work by Erin Walcek Averett, Jeffrey Becker, Catherine Bouras, Anne Chen, Niels Christoffersen, Peter Cobb, Tom Elliott, Jonathan Fu, Greta Hawes, Carolin Johansson, Noah Kaye, Brady Kiesling, Thomas Landvatter, Stanisław Ludwiński, Ingrid Luo, Stephan Maurer, Colin McCaffrey, Gabriel McKee, David Meadows, Gabriel Moss, John Muccigrosso, Gifford Quinn, Rune Rattenborg, Enrico Regazzoni, Rosemary Selth, R. Scott Smith, Richard Talbert, Georgios Tsolakis, and Scott Vanderbilt (see data/changelogs/release.html for details). Included experimental JSON index of links extracted from Pleiades place resources to "toponym" entries in Veronique Chankowski et al. Chronique Des Fouilles En Ligne = Archaeology in Greece Online. Athens: Ecole française d'Athènes and British School at Athens, 2018, together with links to the associated Chronique archaeological reports. See data/indexes/pids2chronique.json. About Pleiades Pleiades is a community-built gazetteer and graph of ancient places. It publishes authoritative information about ancient places and spaces, providing unique services for finding, displaying, and reusing that information under open license. It publishes not just for individual human users, but also for search engines and for the widening array of computational research and visualization tools that support humanities teaching and research. Pleiades is a continuously published scholarly reference work for the 21st century. We embrace the new paradigm of citizen humanities, encouraging contributions from any knowledgeable person and doing so in a context of pervasive peer review. Pleiades welcomes your contribution, no matter how small, and we have a number of useful tasks suitable for volunteers of every interest. Credits Pleiades is brought to you by: Our volunteer content contributors (see data/rdf/authors.ttl for complete list and associated identifiers or data). Pleiades has received significant, periodic support from the National Endowment for the Humanities since 2006. Grant numbers: HK-230973-15, PA-51873-06, PX-50003-08, and PW-50557-10. Any views, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect those of the National Endowment for the Humanities. Additional support has been provided since 2000 by the Ancient World Mapping Center at the University of North Carolina at Chapel Hill. * Development hosting and other project incubation support was provided between 2000 and 2008 by Ross Scaife and the Stoa Consortium. Web hosting and additional support has been provided since 2008 by the Institute for the Study of the Ancient World at New York University.

In scientific research, the ability to effectively retrieve relevant documents based on complex, multifaceted queries is critical. Existing evaluation datasets for this task are limited, primarily due to the high costs and effort required to annotate resources that effectively represent complex queries. To address this, we propose a novel task, Scientific DOcument Retrieval using Multi-level Aspect-based quEries (DORIS-MAE), which is designed to handle the complex nature of user queries in scientific research. Documentations for the DORIS-MAE dataset is publicly available at https://github.com/Real-Doris-Mae/Doris-Mae-Dataset. This upload contains both DORIS-MAE dataset version 1 and ada-002 vector embeddings for all queries and related abstracts (used in candidate pool creation). DORIS-MAE dataset version 1 is comprised of four main sub-datasets, each serving distinct purposes. The Query dataset contains 100 human-crafted complex queries spanning across five categories: ML, NLP, CV, AI, and Composite. Each category has 20 associated queries. Queries are broken down into aspects (ranging from 3 to 9 per query) and sub-aspects (from 0 to 6 per aspect, with 0 signifying no further breakdown required). For each query, a corresponding candidate pool of relevant paper abstracts, ranging from 99 to 138, is provided. The Corpus dataset is composed of 363,133 abstracts from computer science papers, published between 2011-2021, and sourced from arXiv. Each entry includes title, original abstract, URL, primary and secondary categories, as well as citation information retrieved from Semantic Scholar. A masked version of each abstract is also provided, facilitating the automated creation of queries. The Annotation dataset includes generated annotations for all 165,144 question pairs, each comprising an aspect/sub-aspect and a corresponding paper abstract from the query's candidate pool. It includes the original text generated by ChatGPT (version chatgpt-3.5-turbo-0301) explaining its decision-making process, along with a three-level relevance score (e.g., 0,1,2) representing ChatGPT's final decision. Finally, the Test Set dataset contains human annotations for a random selection of 250 question pairs used in hypothesis testing. It includes each of the three human annotators' final decisions, recorded as a three-level relevance score (e.g., 0,1,2). The file "ada_embedding_for_DORIS-MAE_v1.pickle" contains text embeddings for the DORIS-MAE dataset, generated by OpenAI's ada-002 model. The structure of the file is as follows: ├── ada_embedding_for_DORIS-MAE_v1.pickle ├── "Query" │ ├── query_id_1 (Embedding of query_1) │ ├── query_id_2 (Embedding of query_2) │ └── query_id_3 (Embedding of query_3) │ . │ . │ . └── "Corpus" ├── corpus_id_1 (Embedding of abstract_1) ├── corpus_id_2 (Embedding of abstract_2) └── corpus_id_3 (Embedding of abstract_3) . . . The DORIS-MAE dataset is contained in a paper submitted to NeurIPS 2023 Dataset Track for review. Please refer to benchmarking details in the GitHub link: https://github.com/Real-Doris-Mae/Doris-Mae-Dataset. Author information will be made available shortly.

The Knapps Narrows core was drilled at 38.72129N, -76.33162W on the Eastern Shore of Maryland. The cored target interval lies between 84-102 meters in the Nanjemoy Formation. Calcareous nannofossil biozonation was established in conjunction with paleomagnetic data, allowing us to date the interval to approximately 53.7 mya. Benthic foraminiferal and bulk carbon isotope data allowed for further refinement of the dating of this interval, allowing us to identify ETM2 and H2 on the basis of carbon isotope stratigraphy. For analyses of benthic foraminifera 4-6 Anomalinoides acutus specimens were picked from the 180-250 μm size fraction at each interval. Dinoflagellate cyst assemblages were counted in order to allow for paleo-environmental analysis. Benthic foraminiferal δ18O and TEX86 temperature proxies were compiled over the interval of interest. Semi-quantitative clay mineral assemblages were used to test for changes in the weathering response over hyperthermal intervals. There is excellent agreement between the δ18O and TEX86 temperature proxies, although the highest temperatures do not correspond to either hyperthermal event. There is a noticeable increase in illite content during the ETM2 interval, and an absence of kaolinite. Dinoflagellate assemblages suggest changes in marine paleo-environmental conditions were driven by long-term trends rather than immediate responses to the hyperthermal events.