8 research outputs found

    The exceptional finding of Locus 2 at Dehesilla Cave and the Middle Neolithic ritual funerary practices of the Iberian Peninsula

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    There is a significant number of funerary contexts for the Early Neolithic in the Iberian Peninsula, and the body of information is much larger for the Late Neolithic. In contrast, the archaeological information available for the period in between (ca. 4800-4400/4200 cal BC) is scarce. This period, generally called Middle Neolithic, is the least well-known of the peninsular Neolithic sequence, and at present there is no specific synthesis on this topic at the peninsular scale. In 2017, an exceptional funerary context was discovered at Dehesilla Cave (Sierra de Ca ´diz, Southern Iberian Peninsula), providing radiocarbon dates which place it at the beginning of this little-known Middle Neolithic period, specifically between ca. 4800–4550 cal BC. Locus 2 is a deposition constituted by two adult human skulls and the skeleton of a very young sheep/goat, associated with stone structures and a hearth, and a number of pots, stone and bone tools and charred plant remains. The objectives of this paper are, firstly, to present the new archaeological context documented at Dehesilla Cave, supported by a wide range of data provided by interdisciplinary methods. The dataset is diverse in nature: stratigraphic, osteological, isotopic, zoological, artifactual, botanical and radiocarbon results are presented together. Secondly, to place this finding within the general context of the contemporaneous sites known in the Iberian Peninsula through a systematic review of the available evidence. This enables not only the formulation of explanations of the singular new context, but also to infer the possible ritual funerary behaviours and practices in the 5th millennium cal BC in the Iberian Peninsul

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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    Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

    Principles of archaeology

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    Mesolithic Settlement Systems In The Netherlands.

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    PhDArchaeologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/180624/2/7520429.pd

    Large expert-curated database for benchmarking document similarity detection in biomedical literature search

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