Heritage Data Research Articles

Pasto varnish: 3D virtualization of an intangible cultural heritage

The artisanal technique of "barniz de Pasto" (Pasto varnish) in Colombia is an example of an Intangible Cultural Heritage (ICH) that requires urgent safeguarding processes due to a number of factors, including weakened practices, disrupted transmission, and environmental deterioration. The term "Pasto varnish" refers to an ancient artistic tradition from southern Colombia, which was designated as an Intangible Cultural Heritage (ICH) by UNESCO in 2020. This technique entails the decoration of diverse surfaces, including metal and wood, through the application of a thin, malleable film of natural resin derived from the mopa-mopa shrub (Elaeagia pastoensis L.E. Mora), which exhibits the capacity to adhere to a range of surfaces. This article presents the results of the 3D virtualization process of 22 heritage pieces from this technique, which are exhibited on the Virtual Pasto Varnish (VPV) website. The construction of this website entailed the implementation of three domains of virtualization: (i) 3D Documentation, wherein data pertinent to the heritage were collated; (ii) 3D Representation, wherein 3D material corresponding to the heritage artifact was created; and (iii) 3D Dissemination, which facilitated the dissemination and conservation of the 3D data. The results of this project demonstrate that virtual heritage can serve as a collaborative instrument for the integration, safeguarding, and promotion of research on ICH through the data, paradata, and metadata obtained during the 3D virtualization process. This article puts forth the proposition that virtual heritage makes significant contributions to ICH, particularly in regard to the continual recreation of living heritage through self-recognition, the dynamic conservation of its artifacts, and the contextualized research of its data. It can be concluded that for ICH materialized in artifacts—such as the case of Pasto varnish—virtual heritage becomes an important preservation tool when tradition bearers can be actively engaged in the process of "reviving the aura" of the heritage data.

Genetic visualization of cultural heritage: A case study of Zhuxian Town woodblock prints

Using genetic visualization technology to effectively convey cultural information plays an important role in preserving cultural heritage. The key to preserving and developing cultural ecology levels is employing visualization methods and a strategic approach. A valuable tool for conservation, restoration, and cultural gene inheritance, this process involves collecting and processing heritage data. Chinese cultural heritage will be safeguarded, communicated, and promoted using cultural genetic visualization techniques. This technique has the potential to preserve tangible heritage, as demonstrated by Zhuxian Town Woodblock New Year prints. By visualizing and preserving China’s cultural heritage, the results contribute to understanding and engagement with its diverse culture. Heritage can be protected and added value to future generations through cultural genetic visualization techniques.

Enhancing point cloud registration with transformer: cultural heritage protection of the Terracotta Warriors

Point cloud registration technology, by precisely aligning repair components with the original artifacts, can accurately record the geometric shape of cultural heritage objects and generate three-dimensional models, thereby providing reliable data support for the digital preservation, virtual exhibition, and restoration of cultural relics. However, traditional point cloud registration methods face challenges when dealing with cultural heritage data, including complex morphological and structural variations, sparsity and irregularity, and cross-dataset generalization. To address these challenges, this paper introduces an innovative method called Enhancing Point Cloud Registration with Transformer (EPCRT). Firstly, we utilize local geometric perception for positional encoding and combine it with a dynamic adjustment mechanism based on local density information and geometric angle encoding, enhancing the flexibility and adaptability of positional encoding to better characterize the complex local morphology and structural variations of artifacts. Additionally, we introduce a convolutional-Transformer hybrid module to facilitate interactive learning of artifact point cloud features, effectively achieving local–global feature fusion and enhancing detail capture capabilities, thus effectively handling the sparsity and irregularity of artifact point cloud data. We conduct extensive evaluations on the 3DMatch, ModelNet, KITTI, and MVP-RG datasets, and validate our method on the Terracotta Warriors cultural heritage dataset. The results demonstrate that our method has significant performance advantages in handling the complexity of morphological and structural variations, sparsity and irregularity of relic data, and cross-dataset generalization.

DSC-Net: learning discriminative spatial contextual features for semantic segmentation of large-scale ancient architecture point clouds

Semantic segmentation of point cloud data of architectural cultural heritage is of significant importance for HBIM modeling, disease extraction and analysis, and heritage restoration research fields. In the semantic segmentation task of architectural point cloud data, especially for the protection and analysis of architectural cultural heritage, the previous deep learning methods have poor segmentation effects due to the complexity and unevenness of the data, the high geometric feature similarity between different components, and the large scale changes. To this end, this paper proposes a novel encoder-decoder architecture called DSC-Net. It consists of an encoder-decoder structure based on point random sampling and several fully connected layers for semantic segmentation. To overcome the loss of key features caused by random downsampling, DSC-Net has developed two new feature aggregation schemes: the enhanced dual attention pooling module and the global context feature module, to learn discriminative features for the challenging scenes mentioned above. The former fully considers the topology and semantic similarity of neighboring points, generating attention features that can distinguish categories with similar structures. The latter uses spatial location and neighboring volume ratio to provide an overall view of different types of architectural scenes, helping the network understand the spatial relationships and hierarchical structures between different architectural elements. The proposed modules can be easily embedded into various network architectures for point cloud semantic segmentation. We conducted experiments on multiple datasets, including the ancient architecture dataset, the ArCH architectural cultural heritage dataset, and the publicly available architectural segmentation dataset S3DIS. The results show that the mIoU reached 63.56%, 55.84%, and 71.03% respectively. The experimental results prove that our method has the best segmentation effect in dealing with challenging architectural cultural heritage data and also demonstrates its practicality in a wider range of architectural point cloud segmentation applications.

Development of the digital retrieval system integrating intelligent information and improved genetic algorithm: A study based on art museums.

This study aims to develop a digital retrieval system for art museums to solve the problems of inaccurate information and low retrieval efficiency in the digital management of cultural heritage. By introducing an improved Genetic Algorithm (GA), digital management and access efficiency are enhanced, to bring substantial optimization and innovation to the digital management of cultural heritage. Based on the collection of art museums, this study first integrates the collection's images, texts, and metadata with multi-source intelligent information to achieve a more accurate and comprehensive description of digital content. Second, a GA is introduced, and a GA 2 Convolutional Neural Network (GA2CNN) optimization model combining domain knowledge is proposed. Moreover, the convergence speed of traditional GA is improved to adapt to the characteristics of cultural heritage data. Lastly, the Convolutional Neural Network (CNN), GA, and GA2CNN are compared to verify the proposed system's superiority. The results show that in all models, the sample output results' actual value is 2.62, which represents the real data observation results. For sample number 5, compared with the actual value of 2.62, the predicted values of the GA2CNN and GA models are 2.6177 and 2.6313, and their errors are 0.0023 and 0.0113. The CNN model's predicted value is 2.6237, with an error of 0.0037. It can be found that the network fitting accuracy after optimization of the GA2CNN model is high, and the predicted value is very close to the actual value. The digital retrieval system integrated with the GA2CNN model has a good performance in enhancing retrieval efficiency and accuracy. This study provides technical support for the digital organization and display of cultural heritage and offers valuable references for innovative exploration of museum information management in the digital era.

Brazilian-Style Feminicide: Memorialistic Narratives as a Method in Colonial Heritage Data Collection

This paper presents the result of partial research that investigates how the colonial heritages present in Brazilian History and their structural reverberations impact the region context of feminicide in the State of Minas Gerais, outlining a Brazilian reality. Considering the existence of concrete difficulties to hear women who suffer feminicide attempts, based on the memorialistic narratives of women who suffered feminicide attempts, such as the lack of security due to death threats to which they are subjected, this sample counts with a number of fifteen women heard in the city of Belo Horizonte, Minas Gerais, Brazil, appointed by a non-governmental organization that operates in the region, on the combat gender violence. As a data collection technique, it has been chosen the memorialistics narratives because they allow us a more careful way to approach traumatic scenes. In this article, specific, it was analyzed the cases of four of these women with the aim of demonstrate, through the interface between Freudian and Lacanian psychoanalysis and decolonial thinkers, that studies the Afro-Latin American feminism, how violence against these women reveals in their bodies (sometimes, Black) the emergence of a territory to be invade, explored, dominated, and annihilated. It is expected to demonstrate, in the cases presented and through the chosen methodology, the effects of the State´s transgeneratinality and impunity on the naturalization of violence and domination.

Ontology and collaborative data annotation for the standardisation of documentation processes of underground heritage spaces

Progress in the field of documentation and safeguarding of heritage buildings has evolved considerably since the implementation and use of information models. However, these models are being refined and particularised in an attempt to cover the full range of elements to be protected.There is one case that has not yet been sufficiently studied, namely the specific typology of underground built heritage spaces, where the procedures designed for more conventional buildings are not applicable. A lack of consensus has been detected in the standardisation of data processing, which is fundamentally since these spaces do not respond to the classification nor as a building or as an archaeological site.In addition, the morphology of these spaces, -with irregular surfaces of complex geometry-, makes the three-dimensional representation indispensable to record concise and exact documentation.This research proposes a specific methodology for processing heritage data from CIDOC-based models for underground built heritage spaces that fills the identified gap. The research also proposes solutions to the ontological problem of linking information and annotated data, establishing a methodology for performing this kind of work directly on three-dimensional models using web applications. This simplification of the procedure facilitates the reading and multidisciplinary access to the information of the agents involved in safeguarding heritage, replacing two-dimensional projections representation with the introduction of three-dimensional tools.

Novel Class Discovery for Representation of Real-World Heritage Data as Neural Radiance Fields (Student Abstract)

Neural Radiance Fields (NeRF) have been extensively explored as a leading approach for modeling and representing 3D data across various domains. Their ability to capture arbitrary scale point clouds and generate novel views makes them particularly valuable for digitizing cultural heritage sites. However, despite their impressive rendering capabilities, prior methods have often overlooked a significant real-world challenge: handling open-world scenarios characterized by unstructured data containing multiple classes in a single set of unlabeled images. To address this challenge, we propose a novel method NCD-NeRF that leverages Novel-Class Discovery to effectively tackle the complexities inherent in real-world data with unlabeled classes while excelling in producing high-quality NeRF representation. To validate our approach, we conducted a benchmarking analysis using a custom-collected dataset featuring UNESCO World Heritage sites in India. We observe that our proposed NCD-NeRF can parallely discover novel classes and render high-quality 3D volumes.

A review of the Carboniferous shale gas potential of northern England: a data-based analysis of why it won't work

Abstract The recent shale gas revolution originated in the USA in the late 1990s with the exploration of the Carboniferous Barnett Shale in Texas. Success in a number of additional basins in North America, such as the Marcellus, Eagle Ford and Bakken basins, stimulated a search for similar opportunities elsewhere around the world. Among the shales and basins targeted by industry was the Carboniferous Bowland Shale (and equivalents) in northern England. The initial premise that the Barnett Shale represented an excellent analogue for the Bowland Shale led to over-optimistic reserve estimates that have since been shown to be largely incorrect. On the basis of visual inspection of wellbore cores, the Carboniferous Barnett and Bowland shales appear to be very similar. Unfortunately, it is there that the similarity ends. Research carried out for the UK Unconventional Hydrocarbons project has highlighted important differences adversely impacting prospectivity. These can be summarized as basin type/continuity and structural complexity. The total organic carbon, maturity, mineralogy and thickness of the Bowland Shale and equivalents are broadly similar to the successful US examples. Our conclusion is that the Bowland Shale in the UK does not represent a technically significant resource, and in hindsight did not merit the considerable industry and media attention that has been associated with it. One key learning is that fundamental research based on heritage data and modern analytical and modelling techniques should have preceded drilling and fracking operations in northern England.

A Framework Study on the Application of AIGC Technology in the Digital Reconstruction of Cultural Heritage

Abstract AIGC is currently a hot field and a future trend in AI applications, and addressing the challenge of digitally reconstructing cultural heritage under the influence of AI technology is a pressing issue that requires immediate resolution. The article proposes an application framework for AIGC technology that is based on refining its meaning and designing a specific process for applying it to the digital reconstruction of cultural heritage. A high-definition camera is used to acquire relevant images of cultural heritage. The image features are extracted by the SIFT algorithm optimized by the PROSAC algorithm. The color features are acquired by combining the color histogram, color moment, and color correlation diagram. The 3D laser scanning technology is used to obtain the 3D point cloud data of the cultural heritage; the KD-tree improved ICP algorithm is introduced to improve the efficiency of point cloud alignment; the dense reconstruction of the 3D point cloud data of the cultural heritage is realized based on CMVS/PMVS; and the immersive 3D experience system of the cultural heritage is constructed by combining with platforms such as Unity3D. The average matching rate of the optimized SITF algorithm to the image features of cultural heritage is about 74.91%, and the maximum alignment time of the ICP algorithm to the cultural heritage point cloud data based on KD-tree is 9.241 s. The cultural heritage immersive 3D experience system has a satisfaction rate of 56.75%, and the density reconstructed model’s surface has an average deviation of only 0.34 mm from the real surface. The user satisfaction rating for the immersive 3D experience system for cultural heritage is 56.75%. Based on AIGC technology, it can revitalize cultural heritage and achieve digital reconstruction and inheritance innovation of cultural heritage.

The Art of Memory: Archiving Street Art using the CIDOC Conceptual Reference Model

This paper presents a detailed discussion of the data modelling and functional implementation of a semantically-enabled knowledge base related to the creation, digitisation and archiving of large-scale public art works. Based on principles of international best practice, standards and interoperability, the paper focuses on the use of the CIDOC-CRM ontology used in conjunction with the Arches Digital Cultural Heritage data management system to model street art-related entities and their semantic relationships. The work presented here is an extension of the work of the mementoArtem Digital Street Art Archive Project being carried out in conjunction with the INSYTE-Cooley Research Laboratory (I-CRL), South East Technological University (SETU), Waterford, Ireland.

FloraTraiter: Automated parsing of traits from descriptive biodiversity literature.

Plant trait data are essential for quantifying biodiversity and function across Earth, but these data are challenging to acquire for large studies. Diverse strategies are needed, including the liberation of heritage data locked within specialist literature such as floras and taxonomic monographs. Here we report FloraTraiter, a novel approach using rule-based natural language processing (NLP) to parse computable trait data from biodiversity literature. FloraTraiter was implemented through collaborative work between programmers and botanical experts and customized for both online floras and scanned literature. We report a strategy spanning optical character recognition, recognition of taxa, iterative building of traits, and establishing linkages among all of these, as well as curational tools and code for turning these results into standard morphological matrices. Over 95% of treatment content was successfully parsed for traits with <1% error. Data for more than 700 taxa are reported, including a demonstration of common downstream uses. We identify strategies, applications, tips, and challenges that we hope will facilitate future similar efforts to produce large open-source trait data sets for broad community reuse. Largely automated tools like FloraTraiter will be an important addition to the toolkit for assembling trait data at scale.

An Overview of GIS-RS Applications for Archaeological and Cultural Heritage under the DBAR-Heritage Mission

In recent decades, the application of GIS and RS in archaeological and cultural heritage (ACH) has witnessed a notable surge both in terms of quantity and scope. During the initial implementation period (2016–2021) of the Digital Belt and Road Heritage (DBAR-Heritage) working group, several instances of GIS-RS-based applications in support of cultural heritage conservation have merged. In this paper, in order to discuss the great potential of GIS and RS on the Silk Road, an overview of GIS- and RS-based applications in ACH is first presented. In a substantial portion of the published scientific literature, the identification and comprehension of archaeological sites, the monitoring and risk assessment of cultural heritage, and the management and visualization of cultural heritage data are highlighted. Following this, five illustrative case studies from the DBAR-Heritage working group are presented to exemplify how the integration of GIS and RS serves as key approaches in recognizing and appreciating cultural heritage. These selected case studies showcase the utilization of multi-source data for the identification of linear sites; detailed, refined monitoring and assessment of the Angkor Wat heritage; and the reconstruction of the Silk Road routes. These instances serve as the cornerstone for highlighting current trends in GIS and RS applications in ACH along the Silk Road. These methodologies efficiently integrate multi-source geospatial data and employ multidisciplinary approaches, ultimately furnishing sophisticated and intelligent tools for the exploration and management of archaeological and cultural heritage in the era of Big Earth Data. Subsequently, a comprehensive discussion on the merits and challenges of GIS and RS applications in ACH is presented, followed by an exploration of the current application trends. Finally, the prospects for the widespread application of GIS and RS in ACH along the Silk Road are outlined in accordance with the operational plan of DBAR-Heritage during its second implementation phase.

Digital twin applications on cultural world heritage sites in China: A state-of-the-art overview

Recent decades have witnessed digital twin as a toolkit to collect, process, and utilize graphical, spatial and other attribute data of cultural heritage. It has been widely applied on cultural world heritage sites in China, which have significant differences in ages, types, materials, and history of maintenance and conservation. Although specific cases are reported previously, a comprehensive overview is still lacking. This paper explores the association between digital twin and heritage conservation, classifying digital twin techniques into six levels to tackle three tasks: data acquisition, visualization and application. It systematically reviews Chinese heritage digitization practices, considering variations in complexity and purposes, and demonstrates typological and chronological characteristics as well as the composition of stakeholders.

Advancements in 3D Heritage Data Aggregation and Enrichment in Europe: Implications for Designing the Jena Experimental Repository for the DFG 3D Viewer

Since the 2010s, various national and pan-European public infrastructures have been emerging around aggregation, viewing, and 3D heritage model collection. The purpose of this article is to focus on the current state and ecosystem for 3D models in Europe through (a) a review of published studies on users, objects, and demands (b) and an overview of the ecosystem for 3D heritage data. As part of the German distributed infrastructure, the DFG 3D Viewer Jena experimental repository serves as a testbed for technology prototyping and testing. Based on the findings of the European ecosystem, we used this repository to test a prototypic approach to (c) acquiring 3D data from multiple sources, (d) enriching data quality, and (e) enabling indexing, searching, and viewing functionalities.

CLEF. A Linked Open Data Native System for Crowdsourcing

Collaborative data collection initiatives are increasingly becoming pivotal to cultural institutions and scholars, to boost the population of born-digital archives. For over a decade, organisations have been leveraging Semantic Web technologies to design their workflows, ensure data quality, and a means for sharing and reusing (Linked Data). Crucially, scholarly projects that leverage cultural heritage data to collaboratively develop new resources would benefit from agile solutions to simplify the Linked Data production workflow via user-friendly interfaces. To date, only a few pioneers have abandoned legacy cataloguing and archiving systems to fully embrace the Linked Open Data (LOD) paradigm and manage their catalogues or research products through LOD-native management systems. In this article we present Crowdsourcing Linked Entities via web Form (CLEF) , an agile LOD-native platform for collaborative data collection, peer-review, and publication. We detail design choices as motivated by two case studies, from the Cultural Heritage and scholarly domains respectively, and we discuss benefits of our solution in the light of prior works. In particular, the strong focus on user-friendly interfaces for producing FAIR data, the provenance-aware editorial process, and the integration with consolidated data management workflows, distinguish CLEF as a novel attempt to develop Linked Data platforms for cultural heritage.

Extending RiC-O to Model Historical Architectural Archives: The ITDT Ontology

Historical architectural archives enjoy attention from diverse audiences, acting as a primary source of information for architects, historians, public authorities, and common citizens alike. In Italy, the interest in architectural archives has grown slowly but steadily for the last 20 years. However, architectural archives do not generally follow the trend common for museums and galleries in publishing digitized materials and providing standard metadata for individual records. The information that is available online usually includes only an archival finding aid, instead of metadata about the individual records, or fully digital versions of the records. While cataloguing standards for archival descriptions of architectural records have existed at least since the 1980s, the rise of Linked Open Data as a framework for publishing cultural heritage data has allowed archivists to enhance these archival descriptions with richer contextual information and links to external knowledge bases. In this paper we present the ITDT ontology, an extension of the Records in Contexts Ontology that facilitates the representation of architectural records and of the context related to architectural projects, its process, and participating entities. We discuss the application of the ontology to the project files of Italian architect and engineer Dino Tamburini (1924–2011), and the creation of a digital archive offering multiple perspectives over the records.

The ROCEEH Out of Africa Database (ROAD): A large-scale research database serves as an indispensable tool for human evolutionary studies.

Large scale databases are critical for helping scientists decipher long-term patterns in human evolution. This paper describes the conception and development of such a research database and illustrates how big data can be harnessed to formulate new ideas about the past. The Role of Culture in Early Expansions of Humans (ROCEEH) is a transdisciplinary research center whose aim is to study the origins of culture and the multifaceted aspects of human expansions across Africa and Eurasia over the last three million years. To support its research, the ROCEEH team developed an online tool named the ROCEEH Out of Africa Database (ROAD) and implemented its web-based applications. ROAD integrates geographical data as well as archaeological, paleoanthropological, paleontological and paleobotanical content within a robust chronological framework. In fact, a unique feature of ROAD is its ability to dynamically link scientific data both spatially and temporally, thereby allowing its reuse in ways that were not originally conceived. The data stem from published sources spanning the last 150 years, including those generated by the research team. Descriptions of these data rely on the development of a standardized vocabulary and profit from online explanations of each table and attribute. By synthesizing legacy data, ROAD facilitates the reuse of heritage data in novel ways. Database queries yield structured information in a variety of interoperable formats. By visualizing data on maps, users can explore this vast dataset and develop their own theories. By downloading data, users can conduct further quantitative analyses, for example with Geographic Information Systems, modeling programs and artificial intelligence. In this paper, we demonstrate the innovative nature of ROAD and show how it helps scientists studying human evolution to access datasets from different fields, thereby connecting the social and natural sciences. Because it permits the reuse of "old" data in new ways, ROAD is now an indispensable tool for researchers of human evolution and paleogeography.

Aïoli: A reality-based 3D annotation cloud platform for the collaborative documentation of cultural heritage artefacts

Archaeologists, architects, engineers, materials specialists, teachers, curators and restorers of cultural property, contribute to the daily knowledge and conservation of heritage artefacts. For many years, the development of digital technologies has produced important results in the collection, visualisation and indexing of digital resources. Whilst these advances have made it possible to introduce new tools that are making documentation practices evolve within the cultural heritage community, the management of multi-dimensional and multi-format data introduces new problems and challenges, in particular the development of relevant analysis and interpretation methods, the sharing and correlation of heterogeneous data among several actors and contexts, and the centralised archiving of documentation results for long-term preservation purposes. Despite their different approaches and tools for observation, description and analysis, the actors of cultural heritage documentation all have a common interest and central focus: the heritage object, the physical one, whether it is a site, a building, a sculpture, a painting, a work of art, or an archaeological fragment. This is the starting point for the development of “Aïoli”, a reality-based 3D annotation platform, which allows a multidisciplinary community to build semantically-enriched 3D descriptions of heritage artefacts from simple images and spatialised annotations coupled with additional resources. Resulting from more than 10 years of methodological and technological contributions concerning different steps of the workflow starting from reality-based 3D reconstruction to the analysis of cultural heritage data, this platform introduces an innovative framework for the comprehensive, large-scale collaborative documentation of cultural heritage by integrating state-of-the-art technological components within a cloud infrastructure accessible via web interfaces from PCs, tablets and smartphones online and onsite. This paper aims to provide a synthesis of the work done to build it, to describe its main features, and to discuss the limitations and opportunities it raises for cultural heritage studies.

A high time-resolution analysis of the Ground-Level Enhancement (GLE) of 23 February 1956 in terms of the CSHKP standard flare model

The original real-time analog heritage data from the Huancayo recording ionization chamber is differentiated to yield 15–60 s resolution of the first 15 min of the cosmic ray Ground-Level Event (GLE) of 23 February 1956. The initial High-Energy Impulsive (HEI) event of >12.5–20 GeV solar cosmic rays has a rise time of 2 min 15 s with an exponential rise time constant of 49 s, a fluctuating peak pulse of duration 5–7 min, followed by an abrupt decrease of <1 min duration to ∼33% of the peak flux. Detailed analysis of these data shows close consistency with the Carmichael-Sturrock-Hirayama-Kopp-Pneuman (CSHKP) standard flare model. The <49-second acceleration time to >12.5 GeV is explicable in terms of any one of three separate mechanisms: super-Dreier electric acceleration, statistical (Fermi first order), and shock acceleration deep in the corona. The fluctuating solar cosmic ray flux, the abrupt cessation, and the strong cosmic ray anisotropy on arrival at Earth are consistent with the acceleration event consisting of a sequence of short-lived reconnection events within the parent active center. In addition, the HEI event had a duration and time domain profile similar to the gyro-synchrotron emission from electrons associated with the initial acceleration event in the CSHKP model, and the observation of a white light flare towards the end of the microwave pulse. The properties of the GLE-5 HEI event are similar to those accompanying ten other previously studied large GLEs to the west of 240 West on the solar disk. We conclude that the historic high-resolution data from the GLE on 23 February 1956 demonstrate previously unknown properties of the HEI event that are all consistent with cosmic ray acceleration to 25 GeV by the CSHKP and similar flare magnetic reconnection models. A slowly varying and smaller increase commenced 6 min after the commencement of the HEI event and rose steadily to a maximum ∼40 min later. This is consistent with several models of acceleration in association with the coronal mass ejection generated by the flare.