We present the catalog of ∼31,500 extragalactic H i line sources detected by the completed Arecibo Legacy Fast ALFA (ALFALFA) survey out to z < 0.06, including both high signal-to-noise ratio (>6.5) detections and ones of lower quality that coincide in both position and recessional velocity with galaxies of known redshift. We review the observing technique, data reduction pipeline, and catalog construction process, focusing on details of particular relevance to understanding the catalog’s compiled parameters. We further describe and make available the digital H i line spectra associated with the cataloged sources. In addition to the extragalactic H i line detections, we report nine confirmed OH megamasers (OHMs) and 10 OHM candidates at 0.16 < z < 0.22 whose OH line signals are redshifted into the ALFALFA frequency band. Because of complexities in data collection and processing associated with the use of a feed-horn array on a complex single-dish antenna in the terrestrial radio frequency interference environment, we also present a list of suggestions and caveats for consideration by users of the ALFALFA extragalactic catalog for future scientific investigations.

We present the H i content of galaxies in nearby groups and clusters as measured by the 70% complete Arecibo Legacy Fast-ALFA (ALFALFA) survey, including constraints from ALFALFA detection limits. Our sample includes 22 systems at distances between 70 and 160 Mpc over the mass range , for a total of 1986 late-type galaxies. We find that late-type galaxies in the centers of groups lack H i at fixed stellar mass relative to the regions surrounding them. Larger groups show evidence of a stronger dependence of H i properties on environment, despite a similar dependence of color on environment at fixed stellar mass. We compare several environment variables to determine which is the best predictor of galaxy properties; group-centric distance r and are similarly effective predictors, while local density is slightly more effective and group size and halo mass are slightly less effective. While both central and satellite galaxies in the blue cloud exhibit a significant dependence of H i content on local density, only centrals show a strong dependence on stellar mass, and only satellites show a strong dependence on halo mass. Finally, we see evidence that H i is deficient for blue cloud galaxies in denser environments even when both stellar mass and color are fixed. This is consistent with a picture where H i is removed or destroyed, followed by reddening within the blue cloud. Our results support the existence of pre-processing in isolated groups, along with an additional rapid mechanism for gas removal within larger groups and clusters, perhaps ram-pressure stripping.

This paper introduces a novel methodology developed for creating 3D models of archaeological artifacts that reduces the time and effort required by operators. The approach uses a simple vision system mounted on a robotic arm that follows a predetermined path around the object to be reconstructed. The robotic system captures different viewing angles of the object and assigns 3D coordinates corresponding to the robot's pose, allowing it to adjust the trajectory to accommodate objects of various shapes and sizes. The angular displacement between consecutive acquisitions can also be fine-tuned based on the desired final resolution. This flexible approach is suitable for different object sizes, textures, and levels of detail, making it ideal for both large volumes with low detail and small volumes with high detail. The recorded images and assigned coordinates are fed into a constrained implementation of the structure-from-motion (SfM) algorithm, which uses the scale-invariant features transform (SIFT) method to detect key points in each image. By utilising a priori knowledge of the coordinates and SIFT algorithm, low processing time can be ensured while maintaining high accuracy in the final reconstruction.The use of a robotic system to acquire images at a pre-defined pace ensures high repeatability and consistency across different 3D reconstructions, eliminating operator errors in the workflow. This approach not only allows for comparisons between similar objects but also provides the ability to track structural changes of the same object over time.Overall, the proposed methodology provides a significant improvement over current photogrammetry techniques by reducing the time and effort required to create 3D models while maintaining a high level of accuracy and repeatability.

Despite the availability of numerous open datasets on cultural heritage, limited research has focussed on structuring and normalising this type of data, particularly through the extraction of entities from unstructured texts. This step is crucial for enriching, analysing, and understanding these complex datasets. This study presents a procedure designed to streamline the creation of domain-specific datasets for training natural language processing models and evaluates their performance across three distinct datasets generated using this procedure. A zero-shot learning model, the Generalist and Lightweight Model for Named Entity Recognition, was assessed alongside pre-trained spaCy models on three datasets created in the framework of the European Union-funded Research Intelligence Technology for Heritage and Market Security project: one containing provenance information on artefacts from North American museums, another detailing stolen cultural goods in Romania, and a third with structured yet unclassified data on WWII-looted Polish art. Further training of spaCy models on these newly defined datasets revealed that fine-tuned models significantly outperform their non-fine-tuned counterparts, with the best results from the Transformer model fine-tuned on provenance data. This success can be largely attributed to the standardised conventions in provenance research. In contrast, the model fine-tuned on descriptive information performed poorly, likely due to extensive descriptions containing non-essential data that increased model uncertainty. This work highlights the potential of automating entity extraction to build knowledge graphs for cultural object databases, enabling advanced analytical approaches such as Network Analysis.

The current landscape for provenance researchers, cultural heritage crime analysts, and law enforcement working in the culture sector is characterised by the siloing of data across dozens of databases, resulting in fragmented and incomplete resources that must be manually correlated and validated to provide insights into cultural heritage crime. The European Union-funded Research, Intelligence, and Technology for Heritage and Market Security (RITHMS) project is developing a platform to assist law enforcement agencies across Europe in tackling the illicit trafficking of cultural goods by aggregating open, specialised, and police data from a range of sources. This article outlines one of the initial phases of data collection, which has developed 30 tailored web scrapers for the collection of data from existing databases of stolen, missing, protected, and unprovenanced cultural goods. This has resulted in the largest known non-police dataset of these and associated data objects addressing the real-world challenge of data siloes in heritage crime and provenance research. This article details the multi-step process of data collection and pre-processing that has produced the novel consolidated dataset. The mechanism for knowledge discovery developed during this project has immediate applications and has resulted in actionable intelligence for the investigation of cultural goods crimes, highlighting the value of consolidated data as a resource. This research also offers a cursory analysis of the resulting dataset, demonstrating how mining of this resource can enable new scientific insights and offer promising opportunities for intelligence-led policing of cultural heritage crimes.