Deep learning-based image annotation for leukocyte segmentation and classification of blood cell morphology.

The research focuses on the segmentation and classification of leukocytes, a crucial task in medical image analysis for diagnosing various diseases. The leukocyte dataset comprises four classes of images such as monocytes, lymphocytes, eosinophils, and neutrophils. Leukocyte segmentation is achieved through image processing techniques, including background subtraction, noise removal, and contouring. To get isolated leukocytes, background mask creation, Erythrocytes mask creation, and Leukocytes mask creation are performed on the blood cell images. Isolated leukocytes are then subjected to data augmentation including brightness and contrast adjustment, flipping, and random shearing, to improve the generalizability of the CNN model. A deep Convolutional Neural Network (CNN) model is employed on augmented dataset for effective feature extraction and classification. The deep CNN model consists of four convolutional blocks having eleven convolutional layers, eight batch normalization layers, eight Rectified Linear Unit (ReLU) layers, and four dropout layers to capture increasingly complex patterns. For this research, a publicly available dataset from Kaggle consisting of a total of 12,444 images of four types of leukocytes was used to conduct the experiments. Results showcase the robustness of the proposed framework, achieving impressive performance metrics with an accuracy of 97.98% and precision of 97.97%. These outcomes affirm the efficacy of the devised segmentation and classification approach in accurately identifying and categorizing leukocytes. The combination of advanced CNN architecture and meticulous pre-processing steps establishes a foundation for future developments in the field of medical image analysis.

scDOT: enhancing single-cell RNA-Seq data annotation and uncovering novel cell types through multi-reference integration.

The proliferation of single-cell RNA-seq data has greatly enhanced our ability to comprehend the intricate nature of diverse tissues. However, accurately annotating cell types in such data, especially when handling multiple reference datasets and identifying novel cell types, remains a significant challenge. To address these issues, we introduce Single Cell annotation based on Distance metric learning and Optimal Transport (scDOT), an innovative cell-type annotation method adept at integrating multiple reference datasets and uncovering previously unseen cell types. scDOT introduces two key innovations. First, by incorporating distance metric learning and optimal transport, it presents a novel optimization framework. This framework effectively learns the predictive power of each reference dataset for new query data and simultaneously establishes a probabilistic mapping between cells in the query data and reference-defined cell types. Secondly, scDOT develops an interpretable scoring system based on the acquired probabilistic mapping, enabling the precise identification of previously unseen cell types within the data. To rigorously assess scDOT's capabilities, we systematically evaluate its performance using two diverse collections of benchmark datasets encompassing various tissues, sequencing technologies and diverse cell types. Our experimental results consistently affirm the superior performance of scDOT in cell-type annotation and the identification of previously unseen cell types. These advancements provide researchers with a potent tool for precise cell-type annotation, ultimately enriching our understanding of complex biological tissues.

BioKC: a collaborative platform for curation and annotation of molecular interactions.

Curation of biomedical knowledge into systems biology diagrammatic or computational models is essential for studying complex biological processes. However, systems-level curation is a laborious manual process, especially when facing ever-increasing growth of domain literature. New findings demonstrating elaborate relationships between multiple molecules, pathways and cells have to be represented in a format suitable for systems biology applications. Importantly, curation should capture the complexity of molecular interactions in such a format together with annotations of the involved elements and support stable identifiers and versioning. This challenge calls for novel collaborative tools and platforms allowing to improve the quality and the output of the curation process. In particular, community-based curation, an important source of curated knowledge, requires support in role management, reviewing features and versioning. Here, we present Biological Knowledge Curation (BioKC), a web-based collaborative platform for the curation and annotation of biomedical knowledge following the standard data model from Systems Biology Markup Language (SBML). BioKC offers a graphical user interface for curation of complex molecular interactions and their annotation with stable identifiers and supporting sentences. With the support of collaborative curation and review, it allows to construct building blocks for systems biology diagrams and computational models. These building blocks can be published under stable identifiers and versioned and used as annotations, supporting knowledge building for modelling activities.

Approaching Mass Cytometry Translational Studies by Experimental and Data Curation Settings.

Clinical studies are conducted to better understand the pathological mechanism of diseases and to find biomarkers associated with disease activity, drug response, or outcome prediction. Mass cytometry (MC) is a high-throughput single-cell technology that measures hundreds of cells per second with more than 40 markers per cell. Thus, it is a suitable tool for immune monitoring and biomarker discovery studies. Working in translational and clinical settings requires a careful experimental design to minimize, monitor, and correct the variations introduced during sample collection, preparation, acquisition, and analysis. In this review, we will focus on these important aspects of MC-related experiments and data curation in the context of translational clinical research projects.

Community recommendations on cryoEM data archiving and validation.

In January 2020, a workshop was held at EMBL-EBI (Hinxton, UK) to discuss data requirements for the deposition and validation of cryoEM structures, with a focus on single-particle analysis. The meeting was attended by 47 experts in data processing, model building and refinement, validation, and archiving of such structures. This report describes the workshop's motivation and history, the topics discussed, and the resulting consensus recommendations. Some challenges for future methods-development efforts in this area are also highlighted, as is the implementation to date of some of the recommendations.

Creating a Medication Therapy Observational Research Database from an Electronic Medical Record: Challenges and Data Curation.

BACKGROUND: Observational research has shown its potential to complement experimental research and clinical trials by secondary use of treatment data from hospital care processes. It can also be applied to better understand pediatric drug utilization for establishing safer drug therapy. Clinical documentation processes often limit data quality in pediatric medical records requiring data curation steps, which are mostly underestimated. OBJECTIVES: The objectives of this study were to transform and curate data from a departmental electronic medical record into an observational research database. We particularly aim at identifying data quality problems, illustrating reasons for such problems and describing the systematic data curation process established to create high-quality data for observational research. METHODS: Data were extracted from an electronic medical record used by four wards of a German university children's hospital from April 2012 to June 2020. A four-step data preparation, mapping, and curation process was established. Data quality of the generated dataset was firstly assessed following an established 3 × 3 Data Quality Assessment guideline and secondly by comparing a sample subset of the database with an existing gold standard. RESULTS: The generated dataset consists of 770,158 medication dispensations associated with 89,955 different drug exposures from 21,285 clinical encounters. A total of 6,840 different narrative drug therapy descriptions were mapped to 1,139 standard terms for drug exposures. Regarding the quality criterion correctness, the database was consistent and had overall a high agreement with our gold standard. CONCLUSION: Despite large amounts of freetext descriptions and contextual knowledge implicitly included in the electronic medical record, we were able to identify relevant data quality issues and to establish a semi-automated data curation process leading to a high-quality observational research database. Because of inconsistent dosage information in the original documentation this database is limited to a drug utilization database without detailed dosage information.

Image annotation and curation in radiology: an overview for machine learning practitioners.

"Garbage in, garbage out" summarises well the importance of high-quality data in machine learning and artificial intelligence. All data used to train and validate models should indeed be consistent, standardised, traceable, correctly annotated, and de-identified, considering local regulations. This narrative review presents a summary of the techniques that are used to ensure that all these requirements are fulfilled, with special emphasis on radiological imaging and freely available software solutions that can be directly employed by the interested researcher. Topics discussed include key imaging concepts, such as image resolution and pixel depth; file formats for medical image data storage; free software solutions for medical image processing; anonymisation and pseudonymisation to protect patient privacy, including compliance with regulations such as the Regulation (EU) 2016/679 "General Data Protection Regulation" (GDPR) and the 1996 United States Act of Congress "Health Insurance Portability and Accountability Act" (HIPAA); methods to eliminate patient-identifying features within images, like facial structures; free and commercial tools for image annotation; and techniques for data harmonisation and normalisation.Relevance statement This review provides an overview of the methods and tools that can be used to ensure high-quality data for machine learning and artificial intelligence applications in radiology.Key points• High-quality datasets are essential for reliable artificial intelligence algorithms in medical imaging.• Software tools like ImageJ and 3D Slicer aid in processing medical images for AI research.• Anonymisation techniques protect patient privacy during dataset preparation.• Machine learning models can accelerate image annotation, enhancing efficiency and accuracy.• Data curation ensures dataset integrity, compliance, and quality for artificial intelligence development.

Landmark annotation through feature combinations: a comparative study on cephalometric images with in-depth analysis of model's explainability.

OBJECTIVES: The objectives of this study are to explore and evaluate the automation of anatomical landmark localization in cephalometric images using machine learning techniques, with a focus on feature extraction and combinations, contextual analysis, and model interpretability through Shapley Additive exPlanations (SHAP) values. METHODS: We conducted extensive experimentation on a private dataset of 300 lateral cephalograms to thoroughly study the annotation results obtained using pixel feature descriptors including raw pixel, gradient magnitude, gradient direction, and histogram-oriented gradient (HOG) values. The study includes evaluation and comparison of these feature descriptions calculated at different contexts namely local, pyramid, and global. The feature descriptor obtained using individual combinations is used to discern between landmark and nonlandmark pixels using classification method. Additionally, this study addresses the opacity of LGBM ensemble tree models across landmarks, introducing SHAP values to enhance interpretability. RESULTS: The performance of feature combinations was assessed using metrics like mean radial error, standard deviation, success detection rate (SDR) (2 mm), and test time. Remarkably, among all the combinations explored, both the HOG and gradient direction operations demonstrated significant performance across all context combinations. At the contextual level, the global texture outperformed the others, although it came with the trade-off of increased test time. The HOG in the local context emerged as the top performer with an SDR of 75.84% compared to others. CONCLUSIONS: The presented analysis enhances the understanding of the significance of different features and their combinations in the realm of landmark annotation but also paves the way for further exploration of landmark-specific feature combination methods, facilitated by explainability.

Learning Without Real Data Annotations to Detect Hepatic Lesions in PET Images.

OBJECTIVE: Deep neural networks have been recently applied to lesion identification in fluorodeoxyglucose (FDG) positron emission tomography (PET) images, but they typically rely on a large amount of well-annotated data for model training. This is extremely difficult to achieve for neuroendocrine tumors (NETs), because of low incidence of NETs and expensive lesion annotation in PET images. The objective of this study is to design a novel, adaptable deep learning method, which uses no real lesion annotations but instead low-cost, list mode-simulated data, for hepatic lesion detection in real-world clinical NET PET images. METHODS: We first propose a region-guided generative adversarial network (RG-GAN) for lesion-preserved image-to-image translation. Then, we design a specific data augmentation module for our list-mode simulated data and incorporate this module into the RG-GAN to improve model training. Finally, we combine the RG-GAN, the data augmentation module and a lesion detection neural network into a unified framework for joint-task learning to adaptatively identify lesions in real-world PET data. RESULTS: The proposed method outperforms recent state-of-the-art lesion detection methods in real clinical 68Ga-DOTATATE PET images, and produces very competitive performance with the target model that is trained with real lesion annotations. CONCLUSION: With RG-GAN modeling and specific data augmentation, we can obtain good lesion detection performance without using any real data annotations. SIGNIFICANCE: This study introduces an adaptable deep learning method for hepatic lesion identification in NETs, which can significantly reduce human effort for data annotation and improve model generalizability for lesion detection with PET imaging.

Registro español de marcapasos. XX informe oficial de la Asociación del Ritmo Cardiaco de la Sociedad Española de Cardiología (2022) / Spanish pacemaker registry. 20th official report of the Heart Rhythm Association of the Spanish Society of Cardiology (2022)

Introducción y objetivos: En este informe se comunica la actividad de estimulación cardiaca en 2022: número total de implantes, adherencia a la monitorización a distancia, factores demográficos y clínicos y características del material implantado. Métodos: Las fuentes de información son la plataforma CardioDispositivos, la tarjeta europea del paciente portador de marcapasos y los datos facilitados por los fabricantes. Resultados: Las tasas de marcapasos convencionales y resincronizadores de baja energía fueron de 866 y 34 unidades/millón respectivamente. Se implantaron 815 marcapasos sin cables. Se registraron 16.426 procedimientos de 82 hospitales (9.407 a través de CardioDispositivos), lo que supone un 40% de la actividad. La media de edad fue 78,6 años, con predominio de varones (60,3%). El bloqueo auriculoventricular fue el trastorno más frecuente y el 14,5% de los pacientes estaban en fibrilación auricular. Predomina el modo de estimulación DDD/R (55,6%) y la edad influye en el modo de estimulación, de forma que más de un tercio de los pacientes mayores de 80 años en ritmo sinusal recibieron estimulación monocameral en ventrículo. Se incluyeron en monitorización a distancia el 35% de los marcapasos y el 55% de los resincronizadores de baja energía. Conclusiones: Aumentan en un 5,6% el número de marcapasos convencionales, un 16% los resincronizadores de baja energía y un 25% los marcapasos sin cables. Se estabiliza la adherencia a la monitorización a distancia. Aumenta en un 11% el número de procedimientos incluidos en CardioDispositivos, aunque disminuye el volumen de muestra. El uso extensivo de la plataforma es lo que permitirá en años venideros contar con un registro de calidad.(AU)

Introduction and objectives: This article reports the cardiac pacing activity performed in 2022, including the total number of implants, adherence to remote monitoring, demographic and clinical factors, and the characteristics of the implanted devices. Methods: The information sources were the CardioDispositivos online platform, the European pacemaker patient identification card, and data provided by the manufacturers. Results: The rates of conventional pacemakers and low-energy resynchronizers were 866 and 34 units per million population, respectively. A total of 815 leadless pacemakers were implanted. In all, 16426 procedures performed in 82 hospitals were reported (9407 through CardioDispositivos), representing 40% of the activity. The mean age was 78.6 years, with a predominance of men (60.3%). The most frequent disorder was atrioventricular block, and 14.5% of the patients had atrial fibrillation. There was a predominance of the DDD/R pacing mode (55.6%), and pacing mode was influenced by age, such that more than one-third of patients older than 80 years in sinus rhythm received single-chamber ventricular pacing. The remote monitoring program included 35% of conventional pacemakers and 55% of low-energy resynchronization pacemakers. Conclusions: The number of conventional pacemakers increased by 5.6%, low-energy resynchronizers by 16%, and leadless pacemakers by 25%. Adherence to remote monitoring was stable. The number of procedures included in CardioDispositivos increased by 11%, although the sample volume decreased. In the coming years, the widespread use of the platform will likely lead to a high-quality registry.(AU)

Understanding the value of curation: A survey of researcher perspectives of data curation services from six US institutions.

Data curation encompasses a range of actions undertaken to ensure that research data are fit for purpose and available for discovery and reuse, and can help to improve the likelihood that data is more FAIR (Findable, Accessible, Interoperable, and Reusable). The Data Curation Network (DCN) has taken a collaborative approach to data curation, sharing curation expertise across a network of partner institutions and data repositories, and enabling those member institutions to provide expert curation for a wide variety of data types and discipline-specific datasets. This study sought to assess the satisfaction of researchers who had received data curation services, and to learn more about what curation actions were most valued by researchers. By surveying researchers who had deposited data into one of six academic generalist data repositories between 2019-2021, this study set out to collect feedback on the value of curation from the researchers themselves. A total of 568 researchers were surveyed; 42% (238) responded. Respondents were positive in their evaluation of the importance and value of curation, indicating that the participants not only value curation services, but are largely satisfied with the services provided. An overwhelming majority 97% of researchers agreed that data curation adds value to the data sharing process, 96% agreed it was worth the effort, and 90% felt more confident sharing their data due to the curation process. We share these results to provide insights into researchers' perceptions and experience of data curation, and to contribute evidence of the positive impact of curation on repository depositors. From the perspective of researchers we surveyed, curation is worth the effort, increases their comfort with data sharing, and makes data more findable, accessible, interoperable, and reusable.

A Chinese telemedicine-dialogue dataset annotated for named entities.

BACKGROUND: A large collection of dialogues between patients and doctors must be annotated for medical named entities to build intelligence for telemedicine. However, since most patients involved in telemedicine deliver related named entities in informal and long multiword expressions, it is challenging to tag their telemedicine dialogue data. This study aims to address this issue. METHODS: With the telemedicine dialogue dataset for obstetrics and gynecology taken from haodf.com, we developed guidelines and followed a two-round procedure to tag six types of named entities, including disease, symptom, time, pharmaceutical, operation, and examination. Additionally, we developed four deep-learning models based on this dataset to establish a benchmark for named-entity recognition (NER). RESULTS: The distilled obstetrics and gynecology dataset contains 2,383 consultations between doctors and patients, of which 13,411 sentences were from doctors, and 17,929 were from patients. With 63,560 named entities in total, the average number of characters per named entity is 4.33. The experimental results suggest that LatticeLSTM performs best on our dataset in terms of accuracy, precision, recall, and F score. CONCLUSION: Compared with other datasets, this dataset offers three novel facets. This study offers intricately tagged long multiword expressions for medical named entities. Second, this study is one of the first attempts to mark temporal entities in a medical dataset. Third, this annotated dataset is balanced across the six types of labels, which we believe will play a considerable role in expanding telemedicine artificial intelligence.

Registro español de trasplante cardiaco. XXXIV informe oficial de la Asociación de Insuficiencia Cardiaca de la Sociedad Española de Cardiología / Spanish heart transplant registry. 34th official report of the Heart Failure Association of the Spanish Society of Cardiology

Introducción y objetivos: El Registro español de trasplante cardiaco actualiza sus datos anualmente. En este artículo se presentan los datos correspondientes al año 2022.Métodos: Se describen las principales características clínicas, del tratamiento recibido y de los resultados en términos de supervivencia de los procedimientos realizados en 2022, así como las tendencias de estos desde el año 2013.Resultados: En 2022 se han realizado 311 trasplantes cardiacos (un 3,0% más que el año anterior). No se han observado cambios relevantes en las características demográficas y clínicas en 2022 respecto a los años inmediatamente anteriores, lo que confirma las tendencias ya descritas en la última década a una disminución de los procedimientos urgentes y el uso de asistencia circulatoria, sobre todo de dispositivos de asistencia ventricular. En el último decenio, las supervivencias son del 81,4 y el 73,4% a 1 año y a los 3 años, con una mejoría numérica que no ha alcanzado significación estadística.Conclusiones: En la última década se observa una estabilización en las características de los procedimientos de trasplante cardiaco y de sus resultados. Registrado en ClinicalTrial.gov (Identificador: NCT03015311).(AU)

Introduction and objectives: The Spanish heart transplant registry updates its data annually. The current update presents the data for the year 2022.Methods: We describe the main clinical characteristics, treatments received, and survival outcomes including procedures performed in 2022, along with their trends since 2013.Results: In 2022, 311 cardiac transplants were performed, representing a 3.0% increase compared with 2021. Compared with previous years, no significant changes in demographic and clinical characteristics were observed in 2022, confirming the trends identified in the last decade. These trends indicate a decrease in urgent procedures and the use of circulatory support, particularly ventricular assist devices. In the last decade, survival rates at 1 and 3 years were 81.4% and 73.4% respectively, with a slight, nonsignificant improvement.Conclusions: In the last decade, there has been a stabilization in the characteristics of heart transplant procedures and their outcomes. This trial was registered at ClinicalTrial.gov (Identifier: NCT03015311).(AU)

Phylogenomics of weevils revisited: data curation and modelling compositional heterogeneity.

Weevils represent one of the most prolific radiations of beetles and the most diverse group of herbivores on land. The phylogeny of weevils (Curculionoidea) has received extensive attention, and a largely satisfactory framework for their interfamilial relationships has been established. However, a recent phylogenomic study of Curculionoidea based on anchored hybrid enrichment (AHE) data yielded an abnormal placement for the family Belidae (strongly supported as sister to Nemonychidae + Anthribidae). Here we reanalyse the genome-scale AHE data for Curculionoidea using various models of molecular evolution and data filtering methods to mitigate anticipated systematic errors and reduce compositional heterogeneity. When analysed with the infinite mixture model CAT-GTR or using appropriately filtered datasets, Belidae are always recovered as sister to the clade (Attelabidae, (Caridae, (Brentidae, Curculionidae))), which is congruent with studies based on morphology and other sources of molecular data. Although the relationships of the 'higher Curculionidae' remain challenging to resolve, we provide a consistent and robust backbone phylogeny of weevils. Our extensive analyses emphasize the significance of data curation and modelling across-site compositional heterogeneity in phylogenomic studies.

Self-supervised-RCNN for medical image segmentation with limited data annotation.

Many successful methods developed for medical image analysis based on machine learning use supervised learning approaches, which often require large datasets annotated by experts to achieve high accuracy. However, medical data annotation is time-consuming and expensive, especially for segmentation tasks. To overcome the problem of learning with limited labeled medical image data, an alternative deep learning training strategy based on self-supervised pretraining on unlabeled imaging data is proposed in this work. For the pretraining, different distortions are arbitrarily applied to random areas of unlabeled images. Next, a Mask-RCNN architecture is trained to localize the distortion location and recover the original image pixels. This pretrained model is assumed to gain knowledge of the relevant texture in the images from the self-supervised pretraining on unlabeled imaging data. This provides a good basis for fine-tuning the model to segment the structure of interest using a limited amount of labeled training data. The effectiveness of the proposed method in different pretraining and fine-tuning scenarios was evaluated based on the Osteoarthritis Initiative dataset with the aim of segmenting effusions in MRI datasets of the knee. Applying the proposed self-supervised pretraining method improved the Dice score by up to 18% compared to training the models using only the limited annotated data. The proposed self-supervised learning approach can be applied to many other medical image analysis tasks including anomaly detection, segmentation, and classification.

O sigilo médico e os dados sensíveis na telemedicina à luz da Lei Geral de Proteção de Dados / Medical confidentiality and sensitive data in telemedicine in light of the General Data Protection Law / Secret médical et données sensibles en télémédecine au regard de la Loi Générale sur la Protection des Données

A telemedicina, permitida em caráter emergencial durante a covid-19, foi autorizada e regulamentada pela Lei nº 14.510/2022. Reconhecida como serviço imprescindível para a garantia da equidade em saúde, na telemedicina veiculam-se dados considerados sensíveis pela Lei Geral de Proteção de Dados. Este ensaio apresenta uma discussão a respeito de tais dados, os quais detêm relação intrínseca com direitos da personalidade e que devem ser reconhecidos como sigilosos, a fim de garantir o direito à privacidade dos titulares, bem como o respeito ao sigilo médico. Conclui-se que eventual violação dos dados sensíveis pode ensejar sanções administrativas aos agentes de tratamento, mas há divergência doutrinária a respeito do regime de responsabilidade adotado pela Lei Geral de Proteção de Dados, com três possíveis interpretações: responsabilidade objetiva, responsabilidade subjetiva e responsabilidade ativa

Telemedicine, which had been allowed on an emergency basis during covid-19, was authorized and regulat-ed by Law nº 14.510/2022. Recognized as an essential service in guaranteeing equity in health, in telemedi-cine, data considered sensitive by the General Data Protection Law is transmitted. This essay elaborates on a discussion regarding such data, which are intrinsically related to personal rights and must be recognized as confidential in order to ensure the right to privacy of the data subjects, as well as respect for medical confidentiality. It is concluded that any violation of sensitive data may result in administrative sanctions for treatment agents. Still, doctrinal divergence exists regarding the liability regime adopted by the law, with three possible interpretations: strict liability, fault liability, and active liability

La télémédecine, qui avait été permise en urgence pendant le covid-19, a été autorisée et réglementée par la Loi nº 2022-14510. Reconnue comme un service essentiel pour garantir l'équité en santé, en télémédecine, des données considérées comme sensibles par la Loi Générale sur la Protection des Données sont transmises. Cet essai développe une discussion concernant de telles données, qui sont intrinsèquement liées aux droits personnels et doivent être reconnues comme confidentielles afin de garantir le droit à la vie privée des sujets de données, ainsi que le respect de la confidentialité médicale. On en conclut que la violation éventuelle de données sensibles peut entraîner des sanctions administratives pour les agents de traitement. Néanmoins, des divergences doctrinales existent quant au régime de responsabilité adopté par la loi, avec trois interprétations possibles: la responsabilité stricte, la responsabilité pour faute et la responsabilité active

Data Sharing in Chemistry: Lessons Learned and a Case for Mandating Structured Reaction Data.

The past decade has seen a number of impressive developments in predictive chemistry and reaction informatics driven by machine learning applications to computer-aided synthesis planning. While many of these developments have been made even with relatively small, bespoke data sets, in order to advance the role of AI in the field at scale, there must be significant improvements in the reporting of reaction data. Currently, the majority of publicly available data is reported in an unstructured format and heavily imbalanced toward high-yielding reactions, which influences the types of models that can be successfully trained. In this Perspective, we analyze several data curation and sharing initiatives that have seen success in chemistry and molecular biology. We discuss several factors that have contributed to their success and how we can take lessons from these case studies and apply them to reaction data. Finally, we spotlight the Open Reaction Database and summarize key actions the community can take toward making reaction data more findable, accessible, interoperable, and reusable (FAIR), including the use of mandates from funding agencies and publishers.

Public archiving of volume EM data.

Volume electron microscopy (vEM) techniques produce scientifically important datasets which are time and resource intensive to generate (Peddie et al., 2022). Public archival of such datasets, usually described in the literature, provides many benefits to the data depositors, to those making use of research results based on the datasets, and to the vEM community at large, both now and in the future. In this chapter we discuss these benefits, explain how EMBL-EBI's image data services support archival of both vEM and correlative imaging data, and discuss how future developments will unlock more value from these vEM datasets.