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This dataset will contain all the data collected from observation and tracking location of the users participating in the experiments using the CROSSCULT Pilot 1app. It will include information about the users’ interaction with the NG collection information. The dataset will comprise anonymised information about the users who will participate in the NG visits, using the CROSSCULT app. Additionally, for users who give their permission, information can be automatically extracted/retrieved from their devices location. User agreement must be obtained before tracking and processing data about the user. The dataset generated from the observation will provide information on user behaviour. The exact make-up of the fields included in this dataset will be determined as part of the work carried out within CROSSCULT.

This talk tackles images as data in the digital humanities. This visual turn, enabled by a new set of computational methods such as image analysis using computer vision, foregrounds and disputes the logocentrism that has prevailed in digital humanities work over the decades. Abetted by a narrowly construed history of the field, textual computational work has often taken center stage, its longevity propelled by the centrality of particular disciplines to DH. We advocate for a broader role for the image, highlighting the potential of visual resources as the data for analysis and harnessing methods for analyzing images qua images. The talk is organized around three case studies: (1) Distant Viewing Protest Photography, (2) Photogrammar, and (3) Seeing Incunabula: Text as Image. They demonstrate how computational image analysis combined with institutional commitments to digitization along with inter and trans-disciplinary questions can forge avenues of inquiry in the field. We argue that thinking of images as data opens up a more capacious configuration of sources, evidence, and methods in DH. We are sorry that the recording was started only after the welcoming note.

3D model of a tool used for making and repairing nets. Early examples were made of wood or bone which was later replaced by plastic. Dated early-mid 20th century. Physical object held at the Timespan Museum. Part of the Helmsdale Fishing Village reconstruction. A collaborative project between Open Virtual Worlds, a research team within the School of Computer Science, University of St Andrews and Timespan Museum. 3D digitisation was done by Catherine Cassidy and Iain Oliver with archaeological assistance provided by Jacquie Aitken. Digitisation funded by the EU Northern Periphery and Arctic Programme 2014-2020 through the “Connected Culture and Natural Heritage in a Northern Environment” (CINE) project.

Nálezy z archeologického výzkumu v Březnici (okr. Tábor) v letech 2005-2009 a 2019. Výzkum provedl O. Chvojka (Archeologický ústav FF JU v Českých Budějovicích). Data zahrnují údaje o keramice a dalších nálezech použitých k depoziční analýze sídlištních objektů mladší doby bronzové, zejména tzv. žlabů. Výsledky analýzy jsou publikovány v Chvojka et al. 2021. Popis databáze je obsažen v přiloženém PDF souboru. Podpořeno Grantovou agenturou ČR (18-10747S). Finds from the archaeological excavations in Březnice (Tábor district, South Bohemia, Czech Republic) in 2005-2009 and 2019. The fieldwork was directed by O. Chvojka (Institute of Archaeology, South Bohemian University in České Budějovice). Data concern the pottery fragments and other finds (daub, loom weights) used for the analysis of deposition processes in the Late Bronze Age settlement features. Based on this material, a model of house biography and the concept of closing rituals were formulated (see Chvojka et al. 2021). These models suggest an interpretation for the so-called trenches, specific sunken features filled with an unusually rich content of secondary-burnt pottery and other finds. Details of the database are given in the attached PDF file. Supported by the Czech Sceince Foundation (18-10747S). Chvojka, O. – Kuna, M. – Menšík, P. et al. 2021: Rituály ukončení a obnovy. Sídliště mladší doby bronzové v Březnici u Bechyně – Rituals of termination and renewal. The Late Bronze Age settlement in Březnice near Bechyně. České Budějovice – Praha – Plzeň. ISBN 978-80-7394-899-3; ISBN 978-80-7581-039-7; ISBN 978-80-261-1083-5.

The OSDG Community Dataset (OSDG-CD) is a public dataset of thousands of text excerpts, which were validated by approximately 1,000 OSDG Community Platform (OSDG-CP) citizen scientists from over 110 countries, with respect to the Sustainable Development Goals (SDGs). Dataset Information In support of the global effort to achieve the Sustainable Development Goals (SDGs), OSDG is realising a series of SDG-labelled text datasets. The OSDG Community Dataset (OSDG-CD) is the direct result of the work of more than 1,000 volunteers from over 110 countries who have contributed to our understanding of SDGs via the OSDG Community Platform (OSDG-CP). The dataset contains tens of thousands of text excerpts (henceforth: texts) which were validated by the Community volunteers with respect to SDGs. The data can be used to derive insights into the nature of SDGs using either ontology-based or machine learning approaches. 📘 The file contains 40,067 text excerpts and a total of 277,524 assigned labels. Source Data The dataset consists of paragraph-length text excerpts derived from publicly available documents, including reports, policy documents and publication abstracts. A significant number of documents (more than 3,000) originate from UN-related sources such as SDG-Pathfinder and SDG Library. These sources often contain documents that already have SDG labels associated with them. Each text is comprised of 3 to 6 sentences and is about 90 words on average. Methodology All the texts are evaluated by volunteers on the OSDG-CP. The platform is an ambitious attempt to bring together researchers, subject-matter experts and SDG advocates from all around the world to create a large and accurate source of textual information on the SDGs. The Community volunteers use the platform to participate in labelling exercises where they validate each text's relevance to SDGs based on their background knowledge. In each exercise, the volunteer is shown a text together with an SDG label associated with it – this usually comes from the source – and asked to either accept or reject the suggested label. There are 3 types of exercises: All volunteers start with the mandatory introductory exercise that consists of 10 pre-selected texts. Each volunteer must complete this exercise before they can access 2 other exercise types. Upon completion, the volunteer reviews the exercise by comparing their answers with the answers of the rest of the Community using aggregated statistics we provide, i.e., the share of those who accepted and rejected the suggested SDG label for each of the 10 texts. This helps the volunteer to get a feel for the platform. SDG-specific exercises where the volunteer validates texts with respect to a single SDG, e.g., SDG 1 No Poverty. All SDGs exercise where the volunteer validates a random sequence of texts where each text can have any SDG as its associated label. After finishing the introductory exercise, the volunteer is free to select either SDG-specific or All SDGs exercises. Each exercise, regardless of its type, consists of 100 texts. Once the exercise is finished, the volunteer can either label more texts or exit the platform. Of course, the volunteer can finish the exercise early. All progress is saved and recorded still. To ensure quality, each text is validated by up to 9 different volunteers and all texts included in the public release of the data have been validated by at least 3 different volunteers. It is worth keeping in mind that all exercises present the volunteers with a binary decision problem, i.e., either accept or reject a suggested label. The volunteers are never asked to select one or more SDGs that a certain text might relate to. The rationale behind this set-up is that asking a volunteer to select from 17 SDGs is extremely inefficient. Currently, all texts are validated against only one associated SDG label. Column Description doi - Digital Object Identifier of the original document text_id - unique text identifier text - text excerpt from the document sdg - the SDG the text is validated against labels_negative - the number of volunteers who rejected the suggested SDG label labels_positive - the number of volunteers who accepted the suggested SDG label agreement - agreement score based on the formula \(agreement = \frac{|labels_{positive} - labels_{negative}|}{labels_{positive} + labels_{negative}}\) Further Information To learn more about the project, please visit the OSDG website and the official GitHub page. Do not hesitate to share with us your outputs, be it a research paper, a machine learning model, a blog post, or just an interesting observation. All queries can be directed to community@osdg.ai. This CSV file uses UTF-8 character encoding. For easy access on MS Excel, open the file using Data → From Text/CSV. Please split CSV data into different columns by using a TAB delimiter.

Motivo desenhado a partir de ortofoto gerado no Agisoft, com o modelo 3D de levantamento fotogramétrico, no âmbito do Project RARAA- Repositório de Arte Rupestre de Acesso Aberto - COA/OVD/0097/2019 Escudo tipo "Caetra" a ser utilizado pelo guerreiro em combate com o cavaleiro - Sítio da Vermelhosa, Rocha 3, Côa Valley, Portugal

3D model of a broken black-coloured bead. Physical object held at the National Museum of Iceland nr: 2007-36-1884. Found 07/08/2007, found & logged by RGG Part of the Skriðuklaustur Monastery 1550 reconstruction. Digitisation by Open Virtual Worlds, a research team within the School of Computer Science, University of St Andrews, in cooperation with the Gunnar Gunnarsson Institute at Skriðuklaustur and the National Museum of Iceland. 3D digitisation was done by Catherine Cassidy and Iain Oliver with archaeological assistance provided by Skúli Gunnarsson. Funded by the EU Northern Periphery and Arctic Programme 2014-2020 through the “Connected Culture and Natural Heritage in a Northern Environment” (CINE) project.

Gastrophryne carolinensis calling, 08 July 2003.

Code for checking the status of URL links in the dataset: Horton, Laurence (2018). UK Higher Education Institution Research Data Management Policies, 2009-2016. [Data Collection]. Colchester, Essex: UK Data Archive. https://doi.org/10.5255/UKDA-SN-851566 {"references": ["Horton, Laurence (2018). UK Higher Education Institution Research Data Management Policies, 2009-2016. [Data Collection]. Colchester, Essex: UK Data Archive. 10.5255/UKDA-SN-851566"]}

The National Museum of Australia provides access to its collection data through an API. As well as collection items, data is available for parties, places, media, and more. The notebooks in this repository give examples of harvesting, analysing, and visualising data from the NMA collection.