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This dataset captures cultural attitudes towards machine vision technologies as they are expressed in art, games and narratives. The dataset includes records of 500 creative works (including 77 digital games, 191 digital artworks and 236 movies, novels and other narratives) that use or represent machine vision technologies like facial recognition, deepfakes, and augmented reality. The dataset is divided into three main tables, relating to the works, to specific situations in each work involving machine vision technologies, and to the characters that interact with the technologies. Data about each work includes title, author, year and country of publication; types of machine vision technologies featured; topics the work addresses, and sentiments associated with that machine vision usage in the work. In the various works we identified 884 specific situations where machine vision is central. The dataset includes detailed data about each of these situations that describes the actions of human and non-human agents, including machine vision technologies. The dataset is the product of a digital humanities project and can be also viewed as a database at http://machine-vision.no. Data was collected by a team of topic experts who followed an analytical model developed to explore relationships between humans and technologies, inspired by posthumanist and feminist new materialist theories. The project team identified relevant works by searching databases, visiting exhibitions and conferences, reading scholarship, and consulting other experts. The inclusion criteria were creative works( art, games, narratives (movies, novels, etc)) where one of the following machine vision technologies was used in or represented by the work: 3D scans, AI, Augmented reality, Biometrics, Body scans, Camera, Cameraphone, Deepfake, Drones, Emotion recognition, Facial recognition, Filtering, Holograms, Image generation, Interactive panoramas Machine learning, MicroscopeOrTelescope Motion tracking, Non-Visible Spectrum Object recognition, Ocular implant, Satellite images, Surveillance cameras, UGV, Virtual reality, and Webcams. The dataset as well as the more detailed database can be viewed, searched, extracted, or otherwise used or reused and is considered particularly useful for humanities and social science scholars interested in the relationship between technology and culture, and by designers, artists, and scientists developing machine vision technologies. Drupal, 9 R, 4.1.3 RStudio, 2022.02.0 The dataset includes data describing 77 games, 192 artworks and 237 narratives (in total 500 Creative Works) where machine vision technologies play an important role. This includes Creative Works produced between 1891 and 2021, but with a heavy emphasis on recent works: 80% of the Works are from 2011-2021, and just over half from 2016-2021. The Creative Works are from 59 different countries, with 78,6% from North America and Europe, and 21,4% from other parts of the world.

Dataset of variables and results for network and spatial analysis of cult sites, their benefactors and gods in the Hauran (southern Syria) in the Roman period. The dataset includes data tables of cult sites, inscriptions, benefactors, and gods. Additionally, it includes georeferenced vector data of road networks, cult sites, and PPA analysis. PPA (Proximal Point Analysis) uses geographical space to identify the nearest, in this case, three cult sites within a given dataset. PPA is also applied by using roads. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 892166. (Version 2022-11-25) QGIS, 3.16.13 Visone, 2-18 Microsoft Excel, 2019

Dataset of variables and results for spatial network analysis of shortest distances on Roman roads between the proxies for the positions of Roman soldiers and the worship of Asclepius, Apollo, Minerva, and Jupiter, and the positions of Roman physicians in the selected provinces of the Roman Empire. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 892604. QGIS, 3.10.10

Studies of Galactic structure and evolution have benefited enormously from Gaia kinematic information, though additional, intrinsic stellar parameters like age are required to best constrain Galactic models. Asteroseismology is the most precise method of providing such information for field star populations en masse, but existing samples for the most part have been limited to a few narrow fields of view by the CoRoT and Kepler missions. In an effort to provide well-characterized stellar parameters across a wide range in Galactic position, we present the second data release of red giant asteroseismic parameters for the K2 Galactic Archaeology Program (GAP). We provide {nu}_max_ and {Delta}{nu} based on six independent pipeline analyses; first-ascent red giant branch (RGB) and red clump (RC) evolutionary state classifications from machine learning; and ready-to-use radius and mass coefficients, {kappa}_R_ and {kappa}_M_, which, when appropriately multiplied by a solar-scaled effective temperature factor, yield physical stellar radii and masses. In total, we report 4395 radius and mass coefficients, with typical uncertainties of 3.3% (stat.) +/-1% (syst.) for {kappa}_R_ and 7.7% (stat.) +/-2% (syst.) for {kappa}_M_ among RGB stars, and 5.0% (stat.) +/-1% (syst.) for {kappa}_R_ and 10.5% (stat.) +/-2% (syst.) for {kappa}_M_ among RC stars. We verify that the sample is nearly complete- except for a dearth of stars with {nu}_max_<~10-20{mu}Hz-by comparing to Galactic models and visual inspection. Our asteroseismic radii agree with radii derived from Gaia Data Release 2 parallaxes to within 2.2%+/-0.3% for RGB stars and 2.0%+/-0.6% for RC stars. Cone search capability for table J/ApJS/251/23/table2 (*Raw asteroseismic {nu}_max_ and {Delta}{nu} values for K2 Galactic Archaeology Program (GAP) DR2 for each pipeline, with evolutionary states)

This research put the nature and rigidity of linguistic hierarchies to test, taking multiple adjective placement as a case study. We developed an on-line forced choice experiment that measured (i) acceptability judgment ratings and (ii) reaction times, in a big sample of neurotypical, adult speakers of Standard Greek (n=140) and Cypriot Greek (n=30). The task compares what happens when people are asked to process sentences that either comply with or violate allegedly universal ordering constraints that have been described as the outcome of innately wired hierarchies. Our findings do not provide any evidence for a universal hierarchy for adjective ordering that imposes one rigid, unmarked order. We argue that the obtained results are effectively reducing the amount of primitives that are cast as innate, eventually offering a deflationist approach to human linguistic cognition.