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The physical properties of sea ice, including salinity, sea ice temperature, and sea ice texture as well as isotopic composition (oxygen and hydrogen) were analyzed from samples collected on the CAATEX 2019 expedition to the central Arctic Ocean (between North Pole and Svalbard) in late summer 2019. In addition, isotope samples were collected from precipitation, melt ponds, and under-ice seawater. Data was collected to understand the formation mechanisms of sea ice in the area. Ice cores were collected with an ice corer, and measurements (temperature) were directly carried out from ice cores after retrieval. From an additional ice core, samples (salinity and isotopes) were collected and melted onboard. Salinity of samples was measured onboard using a conductivity probe, while samples for isotopic composition were placed in sample bottles and measured at the FARLAB (University of Bergen) after the expedition . A complete ice core was brought back for texture analysis of the sea ice and processed at the freezer lab at the Norwegian Polar Institute. Texture was examined by first slicing ice samples very thin by use of a microtome. These thin ice slices were observed in polarized light that reveals the crystal structure of the ice. This data includes the photographs taken of the ice cores in natural light and in polarized light.

The physical properties of sea ice, including salinity, sea ice temperature, and sea ice texture as well as isotopic composition (oxygen and hydrogen) were analyzed from samples collected on the CAATEX 2019 expedition to the central Arctic Ocean (between North Pole and Svalbard) in late summer 2019. In addition, isotope samples were collected from precipitation, melt ponds, and under-ice seawater. Data was collected to understand the formation mechanisms of sea ice in the area. Ice cores were collected with an ice corer, and measurements (temperature) were directly carried out from ice cores after retrieval. From an additional ice core, samples (salinity and isotopes) were collected and melted onboard. Salinity of samples was measured onboard using a conductivity probe, while samples for isotopic composition were placed in sample bottles and measured at the FARLAB (University of Bergen) after the expedition . A complete ice core was brought back for texture analysis of the sea ice and processed at the freezer lab at the Norwegian Polar Institute. Texture was examined by first slicing ice samples very thin by use of a microtome. These thin ice slices were observed in polarized light that reveals the crystal structure of the ice. This data includes the photographs taken of the ice cores in natural light and in polarized light.

The physical properties of sea ice, including salinity, sea ice temperature, and sea ice texture as well as isotopic composition (oxygen and hydrogen) were analyzed from samples collected on the CAATEX 2019 expedition to the central Arctic Ocean (between North Pole and Svalbard) in late summer 2019. In addition, isotope samples were collected from precipitation, melt ponds, and under-ice seawater. Data was collected to understand the formation mechanisms of sea ice in the area. Ice cores were collected with an ice corer, and measurements (temperature) were directly carried out from ice cores after retrieval. From an additional ice core, samples (salinity and isotopes) were collected and melted onboard. Salinity of samples was measured onboard using a conductivity probe, while samples for isotopic composition were placed in sample bottles and measured at the FARLAB (University of Bergen) after the expedition . A complete ice core was brought back for texture analysis of the sea ice and processed at the freezer lab at the Norwegian Polar Institute. Texture was examined by first slicing ice samples very thin by use of a microtome. These thin ice slices were observed in polarized light that reveals the crystal structure of the ice. This data includes the photographs taken of the ice cores in natural light and in polarized light.

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)

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.

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.

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.