Types Of Entities Research Articles

Knowledge-Enriched Prompt for Low-Resource Named Entity Recognition

Named Entity Recognition (NER) in low-resource settings aims to identify and categorize entities in a sentence with limited labeled data. Although prompt-based methods have succeeded in low-resource perspectives, challenges persist in effectively harnessing information and optimizing computational efficiency. In this work, we present a novel prompt-based method to enhance low-resource NER without exhaustive template tuning. First, we construct knowledge-enriched prompts by integrating representative entities and background information to provide informative supervision tailored to each entity type. Then, we introduce an efficient reverse generative framework inspired by question answering (QA), which avoids redundant computations. Finally, we reduce costs by generating entities from their types while retaining model reasoning capacity. Experiment results demonstrate that our method outperforms other baselines on three datasets under few-shot settings.

Chinese text recognition and knowledge graph of Shen Nong Ben Cao Jing based on BERT pre-trained language models

Background: The research and utilization of ancient traditional Chinese medicine (TCM) books are relatively limited at present. With the rapid development of artificial intelligence (AI), knowledge graph related technology has brought light on this field. Objective: To construct the knowledge graph of Shen Nong Ben Cao Jing, analyze basic knowledge of materia medica, explore implicit knowledge, and conduct visualization display, as well to provide methodological references for the study of ancient TCM books. Methods: The types of knowledge entities and relationships between entities in Shen Nong Ben Cao Jing were analyzed. A training corpus dataset was produced by using the BIO sequence labeling method; a self-developed CNLP text labeling system was used for text labeling; the BERT model was used to recognize named entities; the relationships between entities were set based on rules and semantic associations; the data into the Neo4j-community 4.4.9 graph database was imported by using Cypher language for storage and visualization display after knowledge fusion; finally, a knowledge graph was constructed. Results: The knowledge graph of Shen Nong Ben Cao Jing included 5, 273 nodes and 11, 064 relationships. The schema layer contained 14 entity types and 15 relationship types. Through the query, knowledge can be visualized from the aspects of classification, property, and seven mutual relationships of herbal combination. Conclusion: The knowledge graph constructed in this study directly reflects the knowledge recorded in Shen Nong Ben Cao Jing and the relationship between them, which is suitable for knowledge mining and intuitive multi-dimensional display of ancient TCM books.

Analysis of flow cytometry data from ultrasound-guided lymph node biopsies with two types of needles.

In this study, we combined two techniques, ultrasound-guided needle biopsy and flow cytometry (FCM), to explore their value in patients with enlarged lymph nodes. We compared the results of 198 needle biopsies on FCM and pathology. Forty-two were done by (fine needle aspiration, FNA), and the remaining 156 with (core needle biopsy, CNB), in 36 of 156 patients, a FNA was performed in the same lymph node after completion of the CNB. Except for five types of pathological entities, the rest were differentiated only detected or undetected tumours as the outcome distinction. Among the 198 needle biopsies, 13 were inadequate specimens, while the remaining 185 had pathological findings, including 47 benign and 138 neoplastic findings. Thirty-six patients underwent puncture with both FNA and CNB, both needles produced identical results by FCM, but more cells were obtained by FNA. Among the pathologically positive results, there were 23 missed diagnoses in FCM, in contrast, evidence of tumours was observed in the FCM images of 15 needle biopsies that reported benign or findings that were inconsistent with pathology, and the final diagnosis was consistent with the FCM in 10 cases. FCM detected haematolymphoid tumours with a sensitivity of 87.8% and a specificity of 91.9%. The combination of FCM and ultrasound-guided lymph node needle biopsy can quickly provide guidance for clinical decision-making. We recommend that all lymph node needle biopsies be sent for FCM, the specimen can be obtained by the last puncture with FNA.

Engineering designers’ CAD performance when modelling from isometric and orthographic projections

AbstractThe presented study investigates differences in engineering designers' CAD performance when modelling from two types of projections in technical drawings – isometric and orthographic. The results revealed significant differences in the percentage of correctly replicated components' size and shape, indicating better CAD outcomes when generating CAD models from the orthographic projection. In addition, a comparison of duration, as well as the number and type of sketch entities, sketch relations, and CAD features, showed that CAD modelling processes were similar in both conditions.

Impact and Mechanism of Digital Information Selection on Farmers’ Ecological Production Technology Adoption: A Study on Wheat Farmers in China

The application of ecological techniques by farmers is important for ensuring the environmentally sustainable advancement of the grain sector. Based on micro-level survey data from 921 Chinese wheat growers in the Hebei and Henan provinces, this study employed an endogenous switching probit model and counterfactual analysis to investigate the impact and mechanisms of digital information utilization on ecological production technology adoption. The results indicated that 43.87% of sample wheat farmers had a low level of adoption of ecological techniques. The utilization of digital information significantly promoted farmers’ adoption. If farmers who currently used digital information were to opt-out, the probability of their high adoption would decrease by 11.26%. The utilization of digital information significantly enhanced the adoption of ecological technologies through three mediating factors: technological cognition, production monitoring, and market channels. Therefore, it is imperative to encourage farmers to broaden their social networks and enhance their perception of the importance of digital information. Additionally, it is essential to promote the industrialization and scale operation of wheat production, direct policy subsidies towards new types of management entities, and ensure the accuracy of the supply of digital information for green production through multiple channels. Therefore, it is imperative to expand farmers’ social networks and leverage rural communities to increase their perceived importance of digital information. Governments should increase subsidies and promote the scale and industrialization of wheat production. Moreover, the accuracy of digital information supply for sustainable production should be promoted through digital learning platforms, production monitoring systems, and e-commerce networks.

Principles of risk-based modeling of road traffic injury as a medical and sanitary consequence of a man-made emergency in Ukraine

Background. The provision of modern medical care to victims of road accidents requires the development of standardized approaches. One of the main mechanisms for solving this problem is the formation of scientifically sound clinical protocols and clinical routes for providing medical care, which is essential to creating a model of traffic injury. The purpose of the study was to form a mo­del of traffic trauma in Ukraine as a health-sanitary consequence of an emergency of a man-made nature as a whole and on its basis — a model of providing medical assistance to victims of traffic trauma. Materials and methods. An array of studies of 1696 victims as a result of a traffic accident was formed. Methods of full mo­deling, insoluble randomization, formal logic, and medical statistics were applied. Clinical effective risks were identified. Results. Fundamental principles of modeling road trauma as a health-sanitary consequence of an emergency of a man-made nature as a whole and according to the main types of administrative-territorial entities of Ukraine were presented. The main criterion was the risk of the most serious consequence-the death of the victim, as well as determining the impact of risk-forming factors that did not form clinical-results risk. It has been proven that modeling traffic injury is an effective mechanism for reducing the severity of the medical consequences of road accidents. Conclusions. It is advisable to carry out the formation of models of traffic injury on a hierarchical principle. The principle of modeling significantly optimized making clinical and organizational decisions in the process of providing medical care and allowed to reduce the mortality rate of the victims by 8–11 %.

Every Thing Can Be a Hero! Narrative Visualization of Person, Object, and Other Biographies

Knowledge communication in cultural heritage and digital humanities currently faces two challenges, which this paper addresses: On the one hand, data-driven storytelling in these fields has mainly focused on human protagonists, while other essential entities (such as artworks and artifacts, institutions, or places) have been neglected. On the other hand, storytelling tools rarely support the larger chains of data practices, which are required to generate and shape the data and visualizations needed for such stories. This paper introduces the InTaVia platform, which has been developed to bridge these gaps. It supports the practices of data retrieval, creation, curation, analysis, and communication with coherent visualization support for multiple types of entities. We illustrate the added value of this open platform for storytelling with four case studies, focusing on (a) the life of Albrecht Dürer (person biography), (b) the Saliera salt cellar by Benvenuto Cellini (object biography), (c) the artist community of Lake Tuusula (group biography), and (d) the history of the Hofburg building complex in Vienna (place biography). Numerous suggestions for future research arise from this undertaking.

Comprehensive fault diagnosis for multiple coupled SFCs based on deep learning

Service Function Chains (SFCs) are deployed and executed in a complex network environment, where multiple SFCs are coupled by sharing various types of network functions and transmission links. As a result, diagnosing the state of these SFCs and accurately locating network fault becomes a challenge and plays a fundamental role in SFC management. In this paper, we introduce a comprehensive fault diagnosis mechanism for multiple coupled SFCs by applying the deep learning technique. By formulating the involved network nodes and transmission links into a heterogeneous graph model, we propose the Rendered Service Path Heterogeneous Graph Attention Network (RSP-HAN) model to comprehensively estimate the state of every network node and link. Typically, RSP-HAN first applies Bi-directional Long Short-Term Memory (Bi-LSTM) to normalize features from different types of network entities, then obtains the embedding results through intra-RSP and inter-RSP attention mechanisms. Finally, Multi-Layer Perceptron (MLP) is utilized to estimate the state of these network entities. We also setup an experiment platform and obtain extensive datasets to evaluate our RSP-HAN model. Experimental results show that RSP-HAN performs better in fault diagnosis for various types of network entities than several traditional methods and two deep learning-based baselines, the Graph Convolutional Network (GCN) and the Graph Attention Network (GAT).

The rise of Indo-German collaborative research: 1990–2022

Purpose This study aims to highlight the growth and development of Indo-German collaborative research over the past three decades. Moreover, this study encompasses an in-depth examination of funding acknowledgements to gain valuable insights into the financial support that underpins these collaborative endeavours. Together with this paper, the authors provide an openly accessible data set of Indo-German research papers for further and reproducible research activities (the “Indo-German Literature Dataset”). Design/methodology/approach The data were retrieved from the Web of Science (WoS) database from the year 1990 till the 30th of November 2022. A total of 36,999 records were retrieved against the used query. Acknowledged entities were extracted using a named entity recognition (NER) model specifically trained for this task. Interrelations between the extracted entities and scientific domains, lengths of acknowledgement texts, number of authors and affiliations, number of citations and gender of the first author, as well as collaboration patterns between Indian and German funders were examined. Findings The study reveals a consistent and increasing growth in the publication trend over the years. The study brings to light that Physics, Chemistry, Materials Science, Astronomy and Astrophysics and Engineering prominently dominate the Indo-German collaborative research. The USA, followed by England and France, are the most active collaborators in Indian and German research. Largely, research was funded by major German and Indian funding agencies, international corporations and German and American universities. Associations between the first author’s gender and acknowledged entity were observed. Additionally, relations between entity, entity type and scientific domain were discovered. Practical implications The study paves the way for enhanced collaboration, optimized resource utilization and societal advantages by offering a profound comprehension of the intricacies inherent in research partnerships between India and Germany. Implementation of the insights gleaned from this study holds the promise of cultivating a more resilient and influential collaborative research ecosystem between the two nations. Originality/value The study highlights a deeper understanding of the composition of the Indo-German collaborative research landscape of the past 30 years and its significance in advancing scientific knowledge and fostering international partnerships. Furthermore, the authors provide an open version of the original WoS data set. The Indo-German Literature Data set consists of 22,844 papers from OpenAlex and is available for related studies like literature studies and scientometrics.

Health insurance fraud detection based on multi-channel heterogeneous graph structure learning

Health insurance fraud is becoming more common and impacting the fairness and sustainability of the health insurance system. Traditional health insurance fraud detection primarily relies on recognizing established data patterns. However, with the ever-expanding and complex nature of health insurance data, it is difficult for these traditional methods to effectively capture evolving fraudulent activity and tactics and keep pace with the constant improvements and innovations of fraudsters. As a result, there is an urgent need for more accurate and flexible analytics to detect potential fraud. To address this, the Multi-channel Heterogeneous Graph Structured Learning-based health insurance fraud detection method (MHGSL) was proposed. MHGSL constructs a graph of health insurance data from various entities, such as patients, departments, and medicines, and employs graph structure learning to extract topological structure, features, and semantic information to construct multiple graphs that reflect the diversity and complexity of the data. We utilize deep learning methods such as heterogeneous graph neural networks and graph convolutional neural networks to combine multi-channel information transfer and feature fusion to detect anomalies in health insurance data. The results of extensive experiments on real health insurance data demonstrate that MHGSL achieves a high level of accuracy in detecting potential fraud, which is better than existing methods, and is able to quickly and accurately identify patients with fraudulent behaviors to avoid loss of health insurance funds. Experiments have shown that multi-channel heterogeneous graph structure learning in MHGSL can be very helpful for health insurance fraud detection. It provides a promising solution for detecting health insurance fraud and improving the fairness and sustainability of the health insurance system. Subsequent research on fraud detection methods can consider semantic information between patients and different types of entities.

Railway network delay evolution: A heterogeneous graph neural network approach

Accurate delay evolution prediction plays a pivotal role in train rescheduling decision-making for the railway network. Existing studies on delay prediction predominantly centered around predicting delays for each train in the subsequent stations (i.e., following a train-oriented perspective). Furthermore, train operations in the railway network involve different types of entities (stations, trains, etc.), making the current graph/network models with homogenous nodes (i.e., the same kind of nodes) incapable of effectively capturing the interactions between the entities. This paper develops a network-oriented model to investigate the train delay evolution on railway networks, by predicting the delays of running trains in the network after a given time interval. The proposed model combines the GraphSAGE graph neural network (GNN) and the heterogeneous graph neural network (HetGNN) architecture, thus called SAGE-Het, enabling it to capture interactions between heterogeneous nodes (i.e., different types of nodes) based on different edges (e.g., edges between trains, trains, and stations). Additionally, SAGE-Het allows for flexible inputs in contrast to conventional machine learning techniques, whose inputs must meet the consistent dimension requirement (e.g., in the form of rectangular or grid-like arrays). The performance and robustness of the suggested SAGE-Het model are assessed in experiments on the data from two sub-networks of the China railway network. The experimental results demonstrate that SAGE-Het outperforms the existing delay prediction methods and some advanced HetGNNs used for prediction tasks in other domains; SAGE-Het demonstrates excellent scalability, capable of handling various types of nodes; the predictive performances of SAGE-Het under different prediction time horizons (10/20/30 min ahead) all exhibit better performance over other baselines; the accuracies are over 90 % under the permissible 3-minute errors for the three prediction time horizons. Specifically, the impact of train interactions on delay evolution is investigated based on the flexible input characteristic of the proposed model. The results illustrate that train interactions become subtle with the increase of train headways. This finding directly contributes to decision-making in situations where conflict resolution or train-canceling actions are needed.

Risk identification and management through knowledge Association: A financial event evolution knowledge graph approach

The spread and evolution of financial risk events have endangered the operation of enterprises and financial institutions, and even strike the whole social credit system. To clarify the path of risk events and identify key risk sources by event association, we propose and construct a novel event-driven knowledge graph called “Financial Event Evolution Knowledge Graph (FEEKG)”, with a multi-layer structure of “entity-event-risk”. In the entity layer, the subgraph of about 112,000 entities and 78,500 relationships (including 6 types of entities and 12 types of relations) is constructed based on the event extraction algorithm and topic model. In the event layer, the evolution relationship scores higher than 0.2 is selected as the edge of the event evolution subgraph. The risk layer takes 23 types of topic risk events and 12 types of second-level risk types as nodes and risk transition relations as edges, to generate the dynamic evolution probability subgraphs of topic risk events and risk types. Driven by risk events, we also analyze the characters and rules of entity correlation, event evolution, and risk transmission based on FEEKG; propose a new method for timely mining potential related risk entities and effectively summarizing the regularity of risk evolution and transmission and provide a new perspective for enterprises and financial institutions to find the root of risks and formulate an effective risk management decision in time.

Building a knowledge graph to enrich ChatGPT responses in manufacturing service discovery

Sourcing and identification of new manufacturing partners is crucial for manufacturing system integrators to enhance agility and reduce risk through supply chain diversification in the global economy. The advent of advanced large language models has captured significant interest, due to their ability to generate comprehensive and articulate responses across a wide range of knowledge domains. However, the system often falls short in accuracy and completeness when responding to domain-specific inquiries, particularly in areas like manufacturing service discovery. This research explores the potential of leveraging Knowledge Graphs in conjunction with ChatGPT to streamline the process for prospective clients in identifying small manufacturing enterprises. In this study, we propose a method that integrates bottom-up ontology with advanced machine learning models to develop a Manufacturing Service Knowledge Graph from an array of structured and unstructured data sources, including the digital footprints of small-scale manufacturers throughout North America. The Knowledge Graph and the learned graph embedding vectors are leveraged to tackle intricate queries within the digital supply chain network, responding with enhanced reliability and greater interpretability. The approach highlighted is scalable to millions of entities that can be distributed to form a global Manufacturing Service Knowledge Network Graph that can potentially interconnect multiple types of Knowledge Graphs that span industry sectors, geopolitical boundaries, and business domains. The dataset developed for this study, now publicly accessible, encompasses more than 13,000 manufacturers’ weblinks, manufacturing services, certifications, and location entity types.

Language model based on deep learning network for biomedical named entity recognition

Biomedical Named Entity Recognition (BioNER) is one of the most basic tasks in biomedical text mining, which aims to automatically identify and classify biomedical entities in text. Recently, deep learning-based methods have been applied to Biomedical Named Entity Recognition and have shown encouraging results. However, many biological entities are polysemous and ambiguous, which is one of the main obstacles to the task of biomedical named entity recognition. Deep learning methods require large amounts of training data, so the lack of data also affect the performance of model recognition. To solve the problem of polysemous words and insufficient data, for the task of biomedical named entity recognition, we propose a multi-task learning framework fused with language model based on the BiLSTM-CRF architecture. Our model uses a language model to design a differential encoding of the context, which could obtain dynamic word vectors to distinguish words in different datasets. Moreover, we use a multi-task learning method to collectively share the dynamic word vector of different types of entities to improve the recognition performance of each type of entity. Experimental results show that our model reduces the false positives caused by polysemous words through differentiated coding, and improves the performance of each subtask by sharing information between different entity data. Compared with other state-of-the art methods, our model achieved superior results in four typical training sets, and achieved the best results in F1 values.

CEPTNER: Contrastive learning Enhanced Prototypical network for Two-stage few-shot Named Entity Recognition

Few-shot Named Entity Recognition (NER) systems aim to classify unseen named entity types with limited labeled examples. Significant progress has been made in the use of large-scale pre-trained language models. However, boundary information and hidden relationships between entities beyond the sequence, which provide additional information and play crucial roles in few-shot NER, have received little attention in recent methods that achieve the state of the art. In this paper, we propose CEPTNER, a Contrastive learning Enhanced Prototypical network for Two-stage few-shot Named Entity Recognition, which leverages meta-learning and prototypical network to identify unseen entity types with limited labeled data. Concretely, we first detect candidate boundaries of entities in the stage of boundary detector, then we employ a prototypical network to filter false boundaries and type for the remaining spans in the stage of entity classifier. Additionally, we conduct entity-level contrastive learning to explore the internal relationship between entities that provides extra information while optimizing the prototypical network. CEPTNER is evaluated on two widely-used few-shot NER datasets and a few-shot slot tagging dataset: Few-NERD, CrossNER and SNIPS. Extensive experiments on various benchmarks show the superiority of CEPTNER over previous methods for few-shot NER.

Incorporating Entity Type-Aware and Word–Word Relation-Aware Attention in Generative Named Entity Recognition

Named entity recognition (NER) is a critical subtask in natural language processing. It is particularly valuable to gain a deeper understanding of entity boundaries and entity types when addressing the NER problem. Most previous sequential labeling models are task-specific, while recent years have witnessed the rise of generative models due to the advantage of tackling NER tasks in the encoder–decoder framework. Despite achieving promising performance, our pilot studies demonstrate that existing generative models are ineffective at detecting entity boundaries and estimating entity types. In this paper, a multiple attention framework is proposed which introduces the attention of entity-type embedding and word–word relation into the named entity recognition task. To improve the accuracy of entity-type mapping, we adopt an external knowledge base to calculate the prior entity-type distributions and then incorporate the information input to the model via the encoder’s self-attention. To enhance the contextual information, we take the entity types as part of the input. Our method obtains the other attention from the hidden states of entity types and utilizes it in self- and cross-attention mechanisms in the decoder. We transform the entity boundary information in the sequence into word–word relations and extract the corresponding embedding into the cross-attention mechanism. Through word–word relation information, the method can learn and understand more entity boundary information, thereby improving its entity recognition accuracy. We performed experiments on extensive NER benchmarks, including four flat and two long entity benchmarks. Our approach significantly improves or performs similarly to the best generative NER models. The experimental results demonstrate that our method can substantially enhance the capabilities of generative NER models.

The issue of legal regulation of administrative procedures on the example of foreign countries

The article deals with the issue of legal regulation of administrative procedures on the example of foreign countries. The standards of the administrative procedure regarding the adoption of administrative decisions, i.e. decisions of public administration bodies, which concern the rights and obligations of individuals and legal entities, are considered. The content and peculiarities of legal regulation of administrative procedures in foreign countries, the scope and subject of legal regulation through the prism of the legislation of foreign countries on administrative procedures are outlined. Various approaches to determining the scope, content and methods of its legal regulation are analyzed.
 Attention is focused on the specifics of managerial activity, which must take effective measures in a timely manner in a wide variety of situations, forming the boundaries and restrictions necessary in the rule of law. The types of entities to which administrative procedures apply have been considered.
 The rights and obligations of administrative bodies regarding the preparation and adoption of an administrative decision are defined. Features of appeals by individuals and legal entities, definition of sub-agency category of cases are outlined. The types of decisions made by the administrative body are classified. Emphasis is placed on informing persons whose interests may be affected by an administrative act.
 The grounds for removing officials considered biased are outlined. The rights of citizens participating in the administrative procedure are considered separately.

BioBBC: a multi-feature model that enhances the detection of biomedical entities

The rapid increase in biomedical publications necessitates efficient systems to automatically handle Biomedical Named Entity Recognition (BioNER) tasks in unstructured text. However, accurately detecting biomedical entities is quite challenging due to the complexity of their names and the frequent use of abbreviations. In this paper, we propose BioBBC, a deep learning (DL) model that utilizes multi-feature embeddings and is constructed based on the BERT-BiLSTM-CRF to address the BioNER task. BioBBC consists of three main layers; an embedding layer, a Long Short-Term Memory (Bi-LSTM) layer, and a Conditional Random Fields (CRF) layer. BioBBC takes sentences from the biomedical domain as input and identifies the biomedical entities mentioned within the text. The embedding layer generates enriched contextual representation vectors of the input by learning the text through four types of embeddings: part-of-speech tags (POS tags) embedding, char-level embedding, BERT embedding, and data-specific embedding. The BiLSTM layer produces additional syntactic and semantic feature representations. Finally, the CRF layer identifies the best possible tag sequence for the input sentence. Our model is well-constructed and well-optimized for detecting different types of biomedical entities. Based on experimental results, our model outperformed state-of-the-art (SOTA) models with significant improvements based on six benchmark BioNER datasets.

Challenges and progress in applying space technology in support of the sustainable development goals

The global community, with coordination from the United Nations, is energized to pursue the Sustainable Development Goals (SDGs), a list of 17 important aspirations that summarize the key challenges of our era. The SDGs apply to every nation and represent an international effort to eliminate extreme poverty, ensure access to safe drinking water, strengthen food security, and produce clean and reliable energy, among other pursuits. Space technology is already being used around the world to advance progress toward the SDGs and monitor their related Indicators. This paper explores how six technologies related to space—satellite Earth observation, satellite communication, satellite navigation and positioning, human spaceflight and microgravity research, space technology transfer, and basic scientific research—are being used to realize the vision that the SDGs represent. The paper also discusses the obstacles that limit the application of these technologies for the SDGs and provides an overview of potential paths to overcome these barriers. The paper finally studies the historical and potential roles that four distinct types of entities involved in global sustainable development—governments, non- and inter-governmental organizations, entrepreneurial companies, and universities— have played or may play in the application of space technologies towards the SDGs.

Indigenous research ethics and Tribal Research Review Boards in the United States: examining online presence and themes across online documentation

Researchers seeking to engage in projects related to Tribal communities and their citizens, lands, and non-human relatives are responsible for understanding and abiding by each Tribal nation’s research laws and review processes. Few studies, however, have described the many diverse forms of Tribal research review systems across the United States (US). This study provides one of the most comprehensive examinations of research review processes administered by Tribal Research Review Boards (TRRBs) in the US. Through a systematic analysis, we consider TRRBs’ online presence, online documentation, and themes across documents, for five entity types: Tribal nations and Tribal consortiums, Tribal colleges and universities, Tribal health organizations, Indian Health Services, and other Tribal organizations. Results include an assessment of online presence for 98 potential TRRBs, identification of 118 publicly available online documents, and analysis of 41 themes across four document types: Tribal research codes and TRRBs’ guidelines, applications, and post-approval documents. Altogether, this research provides a macro-level analysis of the most common types of TRRBs in the US in an effort to increase researchers’ understanding of these important processes as they prepare to ethically engage Tribal communities in research. These results aim to empower Tribal decision makers as they align their TRRBs’ online visibility and documentation with community priorities and strengthen their protections for the rights and wellbeing of their citizens and community. Ultimately, by expanding our knowledge of TRRBs across the US, this contribution seeks to uphold Tribal sovereignty in research and promote ethical approaches to research with Indigenous communities.