Large Datasets Research Articles

Deepfake Detection System

Abstract: Because deep fake technology allows for the creation of incredibly realistically modified material that has the potential to mislead viewers and possibly cause instability across a range of businesses, it poses a severe threat to modern society. These days, detecting such modified content is crucial to maintaining the trustworthiness and integrity of digital media. In response, this research proposes a robust deep learning-based technique for detecting deep fakes in videos. Our method uses the Deep fake Detection Challenge dataset, which contains both real and deep fake films, to train and assess our deep learning model. We want to provide a dependable solution that can differentiate between artificially generated information and actual content using stateof-the-art neural networks. Artificial intelligence-generated phony videos are more likely to spread thanks to deep learning algorithms, which raises serious concerns about official blackmail, terrorist propaganda, revenge pornography, and political manipulation. To alleviate these concerns, our approach is designed to automatically identify several forms of deep forgery, including replacement and re-enactment techniques. By means of extensive testing and analysis, we demonstrate the effectiveness of our deep fake detection system in precisely distinguishing bogus films from authentic ones. Our system, which combines cutting-edge deep learning methods with a large dataset, offers a promising way to lessen the hazards related to the spread of deep fake technologies

Text-to-Microstructure Generation Using Generative Deep Learning.

Designing novel materials is greatly dependent on understanding the design principles, physical mechanisms, and modeling methods of material microstructures, requiring experienced designers with expertise and several rounds of trial and error. Although recent advances in deep generative networks have enabled the inverse design of material microstructures, most studies involve property-conditional generation and focus on a specific type of structure, resulting in limited generation diversity and poor human-computer interaction. In this study, a pioneering text-to-microstructure deep generative network (Txt2Microstruct-Net) is proposed that enables the generation of 3D material microstructures directly from text prompts without additional optimization procedures. The Txt2Microstruct-Net model is trained on a large microstructure-caption paired dataset that is extensible using the algorithms provided. Moreover, the model is sufficiently flexible to generate different geometric representations, such as voxels and point clouds. The model's performance is also demonstrated in the inverse design of material microstructures and metamaterials. It has promising potential for interactive microstructure design when associated with large language models and could be a user-friendly tool for material design anddiscovery.

Comparison of speech and music input in North American infants' home environment over the first 2 years of life.

Infants are immersed in a world of sounds from the moment their auditory system becomes functional, and experience with the auditory world shapes how their brain processes sounds in their environment. Across cultures, speech and music are two dominant auditory signals in infants' daily lives. Decades of research have repeatedly shown that both quantity and quality of speech input play critical roles in infant language development. Less is known about the music input infants receive in their environment. This study is the first to compare music input to speech input across infancy by analyzing a longitudinal dataset of daylong audio recordings collected in English-learning infants' home environments, at 6, 10, 14, 18, and 24 months of age. Using a crowdsourcing approach, 643 naïve listeners annotated 12,000 short snippets (10 s) randomly sampled from the recordings using Zooniverse, an online citizen-science platform. Results show that infants overall receive significantly more speech input than music input and the gap widens as the infants get older. At every age point, infants were exposed to more music from an electronic device than an in-person source; this pattern was reversed for speech. The percentage of input intended for infants remained the same over time for music while that percentage significantly increased for speech. We propose possible explanations for the limited music input compared to speech input observed in the present (North American) dataset and discuss future directions. We also discuss the opportunities and caveats in using a crowdsourcing approach to analyze large audio datasets. A video abstract of this article can be viewed at https://youtu.be/lFj_sEaBMN4.

Big data and its impact on the 3Rs: a home cage monitoring oriented review

Undisturbed home cage recording of mouse activity and behavior has received increasing attention in recent years. In parallel, several technologies have been developed in a bid to automate data collection and interpretation. Thanks to these expanding technologies, massive datasets can be recorded and saved in the long term, providing a wealth of information concerning animal wellbeing, clinical status, baseline activity, and subsequent deviations in case of experimental interventions. Such large datasets can also serve as a long-term reservoir of scientific data that can be reanalyzed and repurposed upon need. In this review, we present how the impact of Big Data deriving from home cage monitoring (HCM) data acquisition, particularly through Digital Ventilated Cages (DVCs), can support the application of the 3Rs by enhancing Refinement, Reduction, and even Replacement of research in animals.

An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing.

Technological advances in mass spectrometry and proteomics have made it possible to perform larger-scale and more-complex experiments. The volume and complexity of the resulting data create major challenges for downstream analysis. In particular, next-generation data-independent acquisition (DIA) experiments enable wider proteome coverage than more traditional targeted approaches but require computational workflows that can manage much larger datasets and identify peptide sequences from complex and overlapping spectral features. Data-processing tools such as FragPipe, DIA-NN and Spectronaut have undergone substantial improvements to process spectral features in a reasonable time. Statistical analysis tools are needed to draw meaningful comparisons between experimental samples, but these tools were also originally designed with smaller datasets in mind. This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows. The choice of workflow depends on the user's computational resources. For datasets that are too large to fit into a standard computer's memory, we demonstrate the use of MSstatsBig, a companion R package to MSstats. The protocol also highlights key decisions that have a major effect on both the results and the processing time of the analysis. The MSstats processing can be expected to take 1-3 h depending on the usage of MSstatsBig. The protocol can be run in the point-and-click graphical user interface MSstatsShiny or implemented with minimal coding expertise in R.

Hands-On Neuroinformatics Education at the Crossroads of Online and In-Person: Lessons Learned from NeuroHackademy.

NeuroHackademy ( https://neurohackademy.org ) is a two-week event designed to train early-career neuroscience researchers in data science methods and their application to neuroimaging. The event seeks to bridge the big data skills gap by introducing participants to data science methods and skills that are often ignored in traditional curricula. Such skills are needed for the analysis and interpretation of the kinds of large and complex datasets that have become increasingly important to neuroimaging research due to concerted data collection efforts. In 2020, the event rapidly pivoted from an in-person event to an online event that included hundreds of participants from all over the world. This experience and those of the participants substantially changed our valuation of large online-accessible events. In subsequent events held in 2022 and 2023, we have developed a "hybrid" format that includes both online and in-person participants. We discuss the technical and sociotechnical elements of hybrid events and discuss some of the lessons we have learned while organizing them. We emphasize in particular the role that these events can play in creating a global and inclusive community of practice in the intersection of neuroimaging and data science.

A large and diverse brain organoid dataset of 1,400 cross-laboratory images of 64 trackable brain organoids

Brain organoids represent a useful tool for modeling of neurodevelopmental disorders and can recapitulate brain volume alterations such as microcephaly. To monitor organoid growth, brightfield microscopy images are frequently used and evaluated manually which is time-consuming and prone to observer-bias. Recent software applications for organoid evaluation address this issue using classical or AI-based methods. These pipelines have distinct strengths and weaknesses that are not evident to external observers. We provide a dataset of more than 1,400 images of 64 trackable brain organoids from four clones differentiated from healthy and diseased patients. This dataset is especially powerful to test and compare organoid analysis pipelines because of (1) trackable organoids (2) frequent imaging during development (3) clone diversity (4) distinct clone development (5) cross sample imaging by two different labs (6) common imaging distractors, and (6) pixel-level ground truth organoid annotations. Therefore, this dataset allows to perform differentiated analyses to delineate strengths, weaknesses, and generalizability of automated organoid analysis pipelines as well as analysis of clone diversity and similarity.

A high-resolution seismic velocity model for East Asia using full-waveform tomography: Constraints on India-Asia collisional tectonics

East Asia's complex tectonic history makes it a unique place for plate-tectonic studies. While the crust and mantle structures beneath this region have been extensively investigated by receiver function and many different kinds of tomography studies, discrepancies among seismic velocity models have complicated our understanding of East Asia's tectonic evolution. Here, we use full-waveform adjoint tomography to study the seismic P- and S-wave velocity structures of the crust and mantle beneath East Asia. We use a large waveform dataset from 141 earthquakes recorded by 4640 broadband seismic stations. Our inversion optimizes the normalized correlation coefficient between synthetic and observed seismograms within individual time windows of different seismic phases, from P arrivals to the slowest surface waves. This approach allows us to fit regional multipathed waveforms and provides very well-resolved seismic P- and S-wave velocity structures from the crust to depths of about 800 km. Furthermore, we are able to obtain this resolution over most of East Asia allowing us to compare the amplitude and shapes of anomalies across the entire region. The observed structures in our new full-waveform tomography model (FWEA23) correlate well with tectonic units, revealing sharp contrasts across tectonic boundaries. Our model reveals narrow and strong linear fast anomalies associated with subduction zones in the western Pacific and Southeast Asia. Compared to these oceanic slabs, we observe different and more complicated mantle structures beneath the India-Asia continental collision zone. While our model spans a wide region in East Asia, we focus our interpretations on the seismic velocity anomalies beneath Tibet and surrounding regions, as our model provides significant implications for India-Asia continental collision tectonics. Our analysis suggests that cratonic Indian lithosphere collided with Eurasian plate around 25–20 Ma, subsequently underthrusting beneath Tibet horizontally with little deformation. This resulted in thickening, destabilization, and delamination of the rheologically weaker and less buoyant mantle lithosphere beneath north-central Tibet. Moreover, our model reveals isolated lithospheric fragments within the mantle transition zone and lower mantle beneath southern Tibet and northern India, suggesting that non-cratonic Greater India continental lithosphere has broken off and sunk into the deeper mantle. The lack of continuity among these lithospheric fragments suggests a convective destabilization process, distinct from typical oceanic plate subduction. Our results emphasize the important role of rheological properties of continental lithospheres in shaping the dynamics of continental collision.

Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification

AbstractThe early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination of such conditions. Despite the advancements in Computer-Aided Diagnosis (CADx) systems powered by deep learning, the challenge of accurately classifying brain tumors from MRI scans persists due to the high variability of tumor appearances and the subtlety of early-stage manifestations. This work introduces a novel adaptation of the EfficientNetv2 architecture, enhanced with Global Attention Mechanism (GAM) and Efficient Channel Attention (ECA), aimed at overcoming these hurdles. This enhancement not only amplifies the model’s ability to focus on salient features within complex MRI images but also significantly improves the classification accuracy of brain tumors. Our approach distinguishes itself by meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance in detecting a broad spectrum of brain tumors. Demonstrated through extensive experiments on a large public dataset, our model achieves an exceptional high-test accuracy of 99.76%, setting a new benchmark in MRI-based brain tumor classification. Moreover, the incorporation of Grad-CAM visualization techniques sheds light on the model’s decision-making process, offering transparent and interpretable insights that are invaluable for clinical assessment. By addressing the limitations inherent in previous models, this study not only advances the field of medical imaging analysis but also highlights the pivotal role of attention mechanisms in enhancing the interpretability and accuracy of deep learning models for brain tumor diagnosis. This research sets the stage for advanced CADx systems, enhancing patient care and treatment outcomes.

Analysis of 3760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes

BackgroundRare oncogenic driver events, particularly affecting the expression or splicing of driver genes, are suspected to substantially contribute to the large heterogeneity of hematologic malignancies. However, their identification remains challenging.MethodsTo address this issue, we generated the largest dataset to date of matched whole genome sequencing and total RNA sequencing of hematologic malignancies from 3760 patients spanning 24 disease entities. Taking advantage of our dataset size, we focused on discovering rare regulatory aberrations. Therefore, we called expression and splicing outliers using an extension of the workflow DROP (Detection of RNA Outliers Pipeline) and AbSplice, a variant effect predictor that identifies genetic variants causing aberrant splicing. We next trained a machine learning model integrating these results to prioritize new candidate disease-specific driver genes.ResultsWe found a median of seven expression outlier genes, two splicing outlier genes, and two rare splice-affecting variants per sample. Each category showed significant enrichment for already well-characterized driver genes, with odds ratios exceeding three among genes called in more than five samples. On held-out data, our integrative modeling significantly outperformed modeling based solely on genomic data and revealed promising novel candidate driver genes. Remarkably, we found a truncated form of the low density lipoprotein receptor LRP1B transcript to be aberrantly overexpressed in about half of hairy cell leukemia variant (HCL-V) samples and, to a lesser extent, in closely related B-cell neoplasms. This observation, which was confirmed in an independent cohort, suggests LRP1B as a novel marker for a HCL-V subclass and a yet unreported functional role of LRP1B within these rare entities.ConclusionsAltogether, our census of expression and splicing outliers for 24 hematologic malignancy entities and the companion computational workflow constitute unique resources to deepen our understanding of rare oncogenic events in hematologic cancers.

Training a Dataset Simulated Using RGB Images for an End-to-End Event-Based DoLP Recovery Network

Event cameras are bio-inspired neuromorphic sensors that have emerged in recent years, with advantages such as high temporal resolutions, high dynamic ranges, low latency, and low power consumption. Event cameras can be used to build event-based imaging polarimeters, overcoming the limited frame rates and low dynamic ranges of existing systems. Since events cannot provide absolute brightness intensity in different angles of polarization (AoPs), degree of linear polarization (DoLP) recovery in non-division-of-time (non-DoT) event-based imaging polarimeters is an ill-posed problem. Thus, we need a data-driven deep learning approach. Deep learning requires large amounts of data for training, and constructing a dataset for event-based non-DoT imaging polarimeters requires significant resources, scenarios, and time. We propose a method for generating datasets using simulated polarization distributions from existing red–green–blue images. Combined with event simulator V2E, the proposed method can easily construct large datasets for network training. We also propose an end-to-end event-based DoLP recovery network to solve the problem of DoLP recovery using event-based non-DoT imaging polarimeters. Finally, we construct a division-of-time event-based imaging polarimeter simulating an event-based four-channel non-DoT imaging polarimeter. Using real-world polarization events and DoLP ground truths, we demonstrate the effectiveness of the proposed simulation method and network.

Impact of environmental temperature on the survival outcomes of breast cancer: A SEER-based study.

Experimental evidence in tumor-bearing mouse models shows that exposure to cool, that is, sub-thermoneutral environmental temperature is associated with a higher tumor growth rate and an immunosuppressive tumor immune microenvironment than seen at thermoneutral temperatures. However, the translational significance of these findings in humans is unclear. We hypothesized that breast cancer patients living in warmer climates will have better survival outcomes than patients living in colder climates. A retrospective population-based analysis was conducted on 270,496 stage I-III breast cancer patients, who were retrieved from the Surveillance, Epidemiology and End Results (SEER) over the period from 1996 to 2017. The average annual temperature (AAT) was calculated based on city level data from the National Centers for Environmental Information. A total of 270, 496 patients were analyzed. Temperature as assessed in quartiles. After adjusting for potential confounders, patients who lived in the 3rd and 4th quartile temperature regions with AAT 56.7-62.5°F (3rd quartile) and > 62.5°F (4th quartile) had a 7% increase in the OS compared to patients living at AAT < 48.5°F (1st quartile) (HR 0.93, 95% CI 0.90-0.95 and HR 0.93, 95% CI 0.91-0.96, respectively). For DSS, When comparing AAT quartiles, patients living with AAT in the range of 56.7-62.5°F and > 62.5°F demonstrated a 7% increase each in DSS after adjustment (HR 0.93, 95% CI 0.90-0.96 and HR 0.93, 95% CI 0.90-0.96). Higher environmental temperatures are associated with significantly better OS and DSS in breast cancer patients. Future research is warranted to confirm this observation using large datasets to elucidate the underlying mechanisms and investigate novel therapeutic strategies to minimize this geographic disparity in clinical outcomes.

Krisp: A Python package to aid in the design of CRISPR and amplification-based diagnostic assays from whole genome sequencing data.

Recent pandemics like COVID-19 highlighted the importance of rapidly developing diagnostics to detect evolving pathogens. CRISPR-Cas technology has recently been used to develop diagnostic assays for sequence-specific recognition of DNA or RNA. These assays have similar sensitivity to the gold standard qPCR but can be deployed as easy to use and inexpensive test strips. However, the discovery of diagnostic regions of a genome flanked by conserved regions where primers can be designed requires extensive bioinformatic analyses of genome sequences. We developed the Python package krisp to aid in the discovery of primers and diagnostic sequences that differentiate groups of samples from each other, using either unaligned genome sequences or a variant call format (VCF) file as input. Krisp has been optimized to handle large datasets by using efficient algorithms that run in near linear time, use minimal RAM, and leverage parallel processing when available. The validity of krisp results has been demonstrated in the laboratory with the successful design of a CRISPR diagnostic assay to distinguish the sudden oak death pathogen Phytophthora ramorum from closely related Phytophthora species. Krisp is released open source under a permissive license with all the documentation needed to quickly design CRISPR-Cas diagnostic assays.

Application of a variational autoencoder for clustering and analyzing in situ articular cartilage cellular response to mechanical stimuli.

In various biological systems, analyzing how cell behaviors are coordinated over time would enable a deeper understanding of tissue-scale response to physiologic or superphysiologic stimuli. Such data is necessary for establishing both normal tissue function and the sequence of events after injury that lead to chronic disease. However, collecting and analyzing these large datasets presents a challenge-such systems are time-consuming to process, and the overwhelming scale of data makes it difficult to parse overall behaviors. This problem calls for an analysis technique that can quickly provide an overview of the groups present in the entire system and also produce meaningful categorization of cell behaviors. Here, we demonstrate the application of an unsupervised method-the Variational Autoencoder (VAE)-to learn the features of cells in cartilage tissue after impact-induced injury and identify meaningful clusters of chondrocyte behavior. This technique quickly generated new insights into the spatial distribution of specific cell behavior phenotypes and connected specific peracute calcium signaling timeseries with long term cellular outcomes, demonstrating the value of the VAE technique.

Domain Specific Named Entity Recognition Using Natural Language Processing (NLP) and Software Engineering in Production World

This research is concerned with domain-specific named entity recognition in which certain keywords are withdrawn from the phrase and sentences of the words or paragraph. The project in hand has been effective in different sectors like the insurance companies and hospitals for withdrawing the required keywords in a small amount of time. For instance, a medicine that belongs to a specific keyword is extracted easily and the same occurs with necessary keywords in the case of insurance companies. Advancements in deep neural networks have been established to build the named entity recognition model (NER Model). We have used Spacy and natural language processing for keyword extraction. The future scope of the project is to use LSTM for domain-specific keyword extraction and transfer learning using NLP. A large dataset is required to build the system for keyword extraction since dataset have always been the challenging part of such a system.

Revolutionizing Cybersecurity with Machine Learning: A Comprehensive Review and Future Directions

In the realm of computing, data science has revolutionized cybersecurity operations and technologies. The key to creating automated and intelligent security systems lies in extracting patterns or insights from cybersecurity data and building data-driven models. Data science, encompassing various scientific approaches, machine learning techniques, processes, and systems, studies real-world occurrences through data analysis. Machine learning techniques, known for their flexibility, scalability, and adaptability to new and unknown challenges, have been applied across many scientific fields. Cybersecurity is rapidly expanding due to significant advancements in social networks, cloud and web technologies, online banking, mobile environments, smart grids, and more. Various machine learning techniques have effectively addressed a wide range of computer security issues. This article reviews several machine learning applications in cybersecurity, including phishing detection, network intrusion detection, keystroke dynamics authentication, cryptography, human interaction proofs, spam detection in social networks, smart meter energy consumption profiling, and security concerns associated with machine learning techniques themselves. The methodology involves collecting a large dataset of phishing and legitimate instances, extracting relevant features such as email headers, content, and URLs, and training a machine learning model using supervised learning algorithms. These models can effectively identify phishing emails and websites with high accuracy and low false positive rates. To enhance phishing detection, it is recommended to continuously update the training dataset to include new phishing techniques and employ ensemble methods that combine multiple machine learning models for improved performance

A combined lipidomic and proteomic profiling of Arabidopsis thaliana plasma membrane.

The plant plasma membrane (PM) plays a key role in perception of environmental signals, and set-up of adaptive responses. An exhaustive and quantitative description of the whole set of lipids and proteins constituting the PM is necessary to understand how these components allow to fulfill such essential physiological functions. Here we provide by state-of-the-art approaches the first combined reference of the plant PM lipidome and proteome from Arabidopsis thaliana suspension cell culture. We identified and quantified a reproducible core set of 2165 proteins, which is by far the largest set of available data concerning this plant PM proteome. Using the same samples, combined lipidomic approaches, allowing the identification and quantification of an unprecedented repertoire of 414 molecular species of lipids showed that sterols, phospholipids, and sphingolipids are present in similar proportions in the plant PM. Within each lipid class, the precise amount of each lipid family and the relative proportion of each molecular species were further determined, allowing to establish the complete lipidome of Arabidopsis PM, and highlighting specific characteristics of the different molecular species of lipids. Results obtained point to a finely tuned adjustment of the molecular characteristics of lipids and proteins. More than a hundred proteins related to lipid metabolism, transport, or signaling have been identified and put in perspective of the lipids with which they are associated. This set of data represents an innovative resource to guide further research relative to the organization and functions of the plant PM.

Challenges in multi-centric generalization: phase and step recognition in Roux-en-Y gastric bypass surgery.

Most studies on surgical activity recognition utilizing artificial intelligence (AI) have focused mainly on recognizing one type of activity from small and mono-centric surgical video datasets. It remains speculative whether those models would generalize to other centers. In this work, we introduce a large multi-centric multi-activity dataset consisting of 140 surgical videos (MultiBypass140) of laparoscopic Roux-en-Y gastric bypass (LRYGB) surgeries performed at two medical centers, i.e., the University Hospital of Strasbourg, France (StrasBypass70) and Inselspital, Bern University Hospital, Switzerland (BernBypass70). The dataset has been fully annotated with phases and steps by two board-certified surgeons. Furthermore, we assess the generalizability and benchmark different deep learning models for the task of phase and step recognition in 7 experimental studies: (1) Training and evaluation on BernBypass70; (2) Training and evaluation on StrasBypass70; (3) Training and evaluation on the joint MultiBypass140 dataset; (4) Training on BernBypass70, evaluation on StrasBypass70; (5) Training on StrasBypass70, evaluation on BernBypass70; Training on MultiBypass140, (6) evaluation on BernBypass70 and (7) evaluation on StrasBypass70. The model's performance is markedly influenced by the training data. The worst results were obtained in experiments (4) and (5) confirming the limited generalization capabilities of models trained on mono-centric data. The use of multi-centric training data, experiments (6) and (7), improves the generalization capabilities of the models, bringing them beyond the level of independent mono-centric training and validation (experiments (1) and (2)). MultiBypass140 shows considerable variation in surgical technique and workflow of LRYGB procedures between centers. Therefore, generalization experiments demonstrate a remarkable difference in model performance. These results highlight the importance of multi-centric datasets for AI model generalization to account for variance in surgical technique and workflows. The dataset and code are publicly available at https://github.com/CAMMA-public/MultiBypass140.

Data Plants: Growing Insights in Agriculture through Data Science

Data Science blends different scientific methods to extract valuable insights from large sets of data. Agriculture is crucial for India's economy because more than half of its population relies on it as their main source of income. Research and Extension systems are key in developing and spreading agricultural technologies to boost farmers' earnings. Recently, the use of data has become a game-changer in farming, transforming traditional practices and promoting sustainability efforts. This paper investigates how data-driven methods like machine learning, remote sensing, and predictive analytics are reshaping farming operations and decision-making processes. It also discusses the challenges hindering the widespread adoption of data-driven approaches in farming, such as data security issues and lack of infrastructure. Despite these challenges, there are many opportunities for further exploration and innovation, emphasizing the importance of collaboration across disciplines and policy interventions to maximize the benefits of data-driven approaches for sustainable food production. In summary, this study highlights the significant role of data in farming and its importance in addressing global food security and environmental sustainability challenges.

New Tools for Data Harmonization and Their Potential Applications in Organ Transplantation.

In organ transplantation, accurate analysis of clinical outcomes requires large, high-quality data sets. Not only are outcomes influenced by a multitude of factors such as donor, recipient, and transplant characteristics and posttransplant events but they may also change over time. Although large data sets already exist and are continually expanding in transplant registries and health institutions, these data are rarely combined for analysis because of a lack of harmonization. Promoted by the digitalization of the healthcare sector, effective data harmonization tools became available, with potential applications also for organ transplantation. We discuss herein the present problems in the harmonization of organ transplant data and offer solutions to enhance its accuracy through the use of emerging new tools. To overcome the problem of inadequate representation of transplantation-specific terms, ontologies and common data models particular to this field could be created and supported by a consortium of related stakeholders to ensure their broad acceptance. Adopting clear data-sharing policies can diminish administrative barriers that impede collaboration between organizations. Secure multiparty computation frameworks and the artificial intelligence (AI) approach federated learning can facilitate decentralized and harmonized analysis of data sets, without sharing sensitive data and compromising patient privacy. A common image data model built upon a standardized format would be beneficial to AI-based analysis of pathology images. Implementation of these promising new tools and measures, ideally with the involvement and support of transplant societies, is expected to produce improved integration and harmonization of transplant data and greater accuracy in clinical decision-making, enabling improved patient outcomes.