Structural Database Research Articles

Data Modeling Essentials: Techniques, Best Practices, and Future Trends

Data modeling, in essence, is based on the process of data structuring and organization to effectively handle, analyze, and make informed decisions across organizations. This paper reviews the major concepts and methods significant for modern data architecture in "Data Modeling Essentials: Techniques, Best Practices, and Future Trends.". We start by considering the base techniques, namely Entity-Relationship and Dimensional modeling, and look into their applications in operational and analytical data systems. The paper also covers more advanced topics that include Data Vault modeling for agile data warehousing and NoSQL modeling to accommodate unstructured and semi-structured data, increasingly important in today's big data era. The paper also gives an overview of best practices in developing scalable, flexible data models: stakeholder communication, documentation, and the use of automation tools in accelerating model development and consistency. Data quality, adherence to regulations, and alignment of models with business objectives are among the practices identified as crucial in today's fast-moving data environments. New trends are taking precedence, and data modeling is continuously evolving. We will review the rise of AI- assisted data modeling, graph database structures for highly interconnected data, and model-driven development to accelerate system deployments. This paper examines actionable insights for data professionals, IT teams, and business leaders on how to design robust, agile models that improve the quality of data, enhance decision-making, and foster innovation in an ever-changing data ecosystem. Keywords Data Modeling, Entity-Relationship (ER) Modeling, Dimensional Modeling, Data Quality, Scalable Data Models, Model Driven Development (MDD), Data Management, Structured & Unstructured Data, Future Trends in Data Modeling.

Concept and Design of Cutting Tools for Osseodensification in Implant Dentistry

Osseodensification is an innovative surgical instrumentation technique based on additive (non-cutting) drilling using special burs. It is known from the literature, that the osseodensification burs should operate in a clockwise direction to drill holes and in a counterclockwise direction to compact the osteotomy walls. For these purposes, the burs have special design features, like conical contour shape, increased number of helical flutes, and negative rake angle on the peripheral part. However, although other parameters and features of the burs define their overall performance, they are not described sufficiently, and their influence on surgical quality is almost unknown both for clinicians and tool manufacturers. The purpose of the present research is to identify the key design features of burs for osseodensification and their functional relationship with the qualitative indices of the procedure based on an analytical review of research papers and patent documents. It will help to further improve the design of osseodensification burs and thereby enhance the surgical quality and, ultimately, patient satisfaction. Results: The most important design features and parameters of osseodensification burs are identified. Thereon, the structural model of osseodensification bur is first represented as a hypergraph. Based on the analysis of previous research, functional relationships between design parameters of osseodensification burs, osseodensification procedure conditions, and procedure performance data were established and, for the first time, described in the comprehensive form of a hypergraph. Conclusions: This study provides formal models that form the basis of database structure and its control interface, which will be used in the later developed computer-aided design module to create advanced types of burs under consideration. These models will also help to make good experimental designs used in studies aimed at improving the efficiency of the osseodensification procedure.

Variable stoichiometry and a salt-cocrystal intermediate in multicomponent systems of flucytosine: structural elucidation and their impact on stability.

New cocrystals and a salt-cocrystal intermediate system involving the antifungal drug flucytosine (FCY) and various coformers including caffeic acid (CAF), 2-chloro-4-nitrobenzoic acid (CNB), hydroquinone (HQN), resorcinol (RES) and catechol (CAL), are reported. The crystal structures of the prepared multicomponent systems were determined through SC-XRD analysis and characterized by different solid-state techniques. All FCY multicomponent systems crystallize in anhydrous form with different stoichiometric ratios. The cocrystals FCY-HQN, FCY-RES and FCY-CAL crystallize in 2:0.5, 2:0.5 and 3:2 stoichiometric ratios respectively. In contrast, FCY-CAF and FCY-CNB crystallize in a 1:1 stoichiometric ratio. The FCY-CAF cocrystal is formed via an acid-pyrimidine heterosynthon. Due to the partial proton transfer from the acid group of CNB to FCY, a three-point homosynthon is observed between two FCY molecules and the molecules interact via an N-H...O hydrogen bond between FCY and CNB. In FCY phenolic cocrystals, a single-point O-H...O hydrogen bond is observed. The formation of cocrystals and salt-cocrystal intermediate was further confirmed by difference Fourier map analysis and bond angle differences. Except for FCY-CAL, all the multicomponent systems were reproduced in the bulk scale for further characterization. A detailed Crystal Structural Database search was carried out on the multicomponent systems of FCY with acid coformers and we evaluated the formation of cocrystals/salt based on the ΔpKa values, the difference in the bond distances and bond angles. Additionally, the prepared multicomponent systems exhibited hydration stability for one month under accelerated conditions [40 (2)°C and relative humidity 90-95 (5)%].

Using In Silico Methods to Identify Protein Tyrosine Kinase A (PtkA) Homolog in Non-Tuberculous Mycobacteria (NTM)

Non-tuberculous mycobacteria (NTM) represent a diverse group of mycobacterial species known for causing opportunistic infections, especially in individuals with underlying health conditions. Unlike Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, NTM species exhibit different pathogenic characteristics and drug resistance mechanisms, making them increasingly relevant in clinical settings. PtkA is a crucial protein tyrosine kinase that regulates bacterial growth, stress response, and virulence by phosphorylating various substrates in Mtb. Understanding whether PtkA homologs exist in NTM could provide insights into their virulence and resistance mechanisms. In silico approaches, which utilize computational tools for sequence alignment, structure prediction, and functional annotation, offer a powerful means to identify homologous proteins across different species. In this article, we have employed tools like BLAST (Basic Local Alignment Search Tool), protein structure databases, and the NTM database to identify PtkA homologs in NTM genomes, providing a foundation for further studies.

Zmiany entropii terenów zabudowanych w otoczeniu Krakowa w latach 2014-2020

Information on landscape structure is an important issue for sustainable development and for making correct spatial planning decisions. Therefore, studies providing information on the diversity and changes of land cover and land use in a selected area are currently an important research topic. In addition to quantitative-qualitative statistics, spatial entropy is increasingly used to assess the degree of diversity of land cover and land use. This paper aim was to determine the diversity of objects within the land cover classes in the years 2014, 2017 and 2020, with a focus on built-up areas, in three districts bordering the city of Kraków, i.e. the Wieliczka, Kraków and Proszowice districts, located in the Małopolskie Voivodeship in Poland. In order to perform the analysis with the appropriate spatial resolution, a hex grid was used to determine the diversity of objects in the built-up area class. For this reason, data on land cover from the BDOT10k national database of topographic objects were used. The analyses followed the division into classes and objects defined in the structure of the BDOT10k database. The degree of diversity was determined using the Entropy Index and Herfindahl-Hirschman Index. Changes in the diversity of built-up areas were observed especially in areas located in close proximity to the border of Kraków, as well as on its eastern and south-eastern sides. A significant relationship was also observed between the increase in diversity of objects in the built-up area class and the immediate proximity of roads.

BFVD-a large repository of predicted viral protein structures.

The AlphaFold Protein Structure Database (AFDB) is the largest repository of accurately predicted structures with taxonomic labels. Despite providing predictions for over 214 million UniProt entries, the AFDB does not cover viral sequences, severely limiting their study. To address this, we created the Big Fantastic Virus Database (BFVD), a repository of 351 242 protein structures predicted by applying ColabFold to the viral sequence representatives of the UniRef30 clusters. By utilizing homology searches across two petabases of assembled sequencing data, we improved 36% of these structure predictions beyond ColabFold's initial results. BFVD holds a unique repertoire of protein structures as over 62% of its entries show no or low structural similarity to existing repositories. We demonstrate how a substantial fraction of bacteriophage proteins, which remained unannotated based on their sequences, can be matched with similar structures from BFVD. In that, BFVD is on par with the AFDB, while holding nearly three orders of magnitude fewer structures. BFVD is an important virus-specific expansion to protein structure repositories, offering new opportunities to advance viral research. BFVD can be freely downloaded at bfvd.steineggerlab.workers.dev and queried using Foldseek and UniProt labels at bfvd.foldseek.com.

CATH v4.4: major expansion of CATH by experimental and predicted structural data.

CATH (https://www.cathdb.info) is a structural classification database that assigns domains to the structures in the Protein Data Bank (PDB) and AlphaFold Protein Structure Database (AFDB) and adds layers of biological information, including homology and functional annotation. This article covers developments in the CATH classification since 2021. We report the significant expansion of structural information (180-fold) for CATH superfamilies through classification of PDB domains and predicted domain structures from the Encyclopedia of Domains (TED) resource. TED provides information on predicted domains in AFDB. CATH v4.4 represents an expansion of ∼64 844 experimentally determined domain structures from PDB. We also present a mapping of ∼90 million predicted domains from TED to CATH superfamilies. New PDB and TED data increases the number of superfamilies from 5841 to 6573, folds from 1349 to 2078 and architectures from 41 to 77. TED data comprises predicted structures, so these new folds and architectures remain hypothetical until experimentally confirmed. CATH also classifies domains into functional families (FunFams) within a superfamily. We have updated sequences in FunFams by scanning FunFam-HMMs against UniProt release 2024_02, giving a 276% increase in FunFams coverage. The mapping of TED structural domains has resulted in a 4-fold increase in FunFams with structural information.

Predicting co-crystal structures of N-halide phthalimides with 3,5-dimethylpyridine.

Crystal structure prediction (CSP) calculations were carried out to examine potential formation of co-crystals between N-halide phthalimides (Cl, Br or I) and 3,5-dimethylpyridine (35DMP). The co-crystal structure of N-bromophthalimide (nbp) with 35DMP (nbp-35DMP) is known, and the generated co-crystal structure of rank 1 is identical to experimental structure (VELXES). For the unknown crystal structure of N-iodophthalimide (nip), structure of rank 1 is suggested as a likely co-crystal structure. On the other hand, our calculations suggest the improbability of co-crystal formation between ncp and 35DMP. The CSP findings indicate that strong N-X...N interactions consistent with similar experimental structures in the Cambridge Structural Database play a major role in crystal structures of the studied compounds.

InterPro: the protein sequence classification resource in 2025.

InterPro (https://www.ebi.ac.uk/interpro) is a freely accessible resource for the classification of protein sequences into families. It integrates predictive models, known as signatures, from multiple member databases to classify sequences into families and predict the presence of domains and significant sites. The InterPro database provides annotations for over 200 million sequences, ensuring extensive coverage of UniProtKB, the standard repository of protein sequences, and includes mappings to several other major resources, such as Gene Ontology (GO), Protein Data Bank in Europe (PDBe) and the AlphaFold Protein Structure Database. In this publication, we report on the status of InterPro (version 101.0), detailing new developments in the database, associated web interface and software. Notable updates include the increased integration of structures predicted by AlphaFold and the enhanced description of protein families using artificial intelligence. Over the past two years, more than 5000 new InterPro entries have been created. The InterPro website now offers access to 85000 protein families and domainsfrom its member databases and serves as a long-term archive for retired databases. InterPro data, software and tools are freely available.

GLN2 as a key biomarker and therapeutic target: evidence from a comprehensive pan-cancer study using molecular, functional, and bioinformatic analyses

BackgroundGNL2, a nuclear protein, is involved in ribosome production and cell cycle regulation. However, its expression and function in different types of tumors are not well understood. Comprehensive studies across multiple cancer types are needed to assess the potential of GNL2 as a diagnostic, prognostic, and immunological marker.MethodsmRNA expression data, copy number alteration threshold data, masked copy number segmentation data, and DNA methylation 450 K data from The Cancer Genome Atlas (TCGA) pan-cancer cohort were obtained from the Firehose database. Additional data, including miRNA, The Cancer Proteome Atlas (TCPA), mutation data, and clinical information, were sourced from the University of California Santa Cruz (UCSC) Xena database. The cBioPortal database facilitates the examination of GNL2 mutation frequency, location, and 3D structure in the TCGA database. Gene Expression Omnibus (GEO) data verified the transcriptome level expression in the TCGA cohort. Protein expression levels were analyzed via the Human Protein Atlas (HPA) database and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Gene set enrichment analysis (GSEA) was employed to investigate the biological role of GNL2 across cancers. Multiple immune infiltration algorithms from the TIMER2.0 database were utilized to examine the correlation between GNL2 expression and the tumor immune microenvironment. The transcriptome-wide immune infiltration results were validated using 72 single-cell datasets from the Tumor Immune Single-cell Hub (TISCH) database. Pan-cancer survival maps were constructed, and GNL2 expression in different molecular subtypes across cancers was examined. The relationship between GNL2 and drug resistance was investigated using data from CellMiner, GDSC, and CTRP. The Comparative Toxicogenomics Database (CTD) was used to identify chemicals affecting GNL2 expression.ResultsGNL2 is located primarily in the nucleus, and its expression is regulated mainly through somatic copy number alteration (SCNA) and aberrant DNA methylation, according to TCGA data. Database analysis and immunohistochemical results from clinical samples revealed high GNL2 expression in most tumors, which was correlated with diagnostic significance. High GNL2 expression often indicates a poor prognosis with pan-cancer prognostic value. Gene set enrichment analysis (GSEA) suggested that GNL2 is involved in tumor development through cell proliferation-related pathways. GNL2 expression is correlated with the expression of immune-related genes and the infiltration levels of multiple immune cells. The relationships between GNL2 and various drugs and chemicals were examined, revealing its influence on drug sensitivity and identifying five chemicals countering GNL2-mediated pro-cancer effects.ConclusionComprehensive bioinformatics analysis of GNL2 in pan-cancer tissues, combined with experimental validation, elucidated the pan-cancer expression pattern of GNL2, determined its diagnostic and prognostic value, and explored the biological functions of GNL2. GNL2 may be involved in the regulation of cell cycle progression and remodeling of the tumor microenvironment and is associated with poor prognosis as a risk factor in most tumors. The potential of GNL2-based cancer therapies is emphasized, assisting in predicting the response to chemotherapy.

Band structure database of layered intercalation compounds with various intercalant atoms and layered hosts

Here we provide a database comprising electronic band structures of 9,004 layered intercalation compounds, where atoms are intercalated into a host layered compound with different intercalant atoms, along with 468 structures related to the layered host compounds. Additionally, we provide properties derived from the electronic states such as band gap as well as stability-related properties like formation energies. Direct comparison of the band structures before and after intercalation is generally challenging due to changes in their space group and k-path. However, in this study, we developed new k-paths consistent with the host materials, allowing for the direct comparison of band structures before and after intercalation. This enables direct and quantitative discussion of the band structure changes induced by the intercalations and provides a valuable database for intercalant-driven band engineering. Layered intercalation compounds are widely used in many fields, including superconductivity and energy applications, and understanding of electronic structures is necessary. The feature of our database holds promises for the development of layered compounds with enhanced functionalities through database utilization.

Concept model and realization of object orientied system with metaprogramming

The purpose of the research is to develop a conceptual model of an object oriented system and implement it using a functional language with metaprogramming support. Methods. A conceptual model of an object oriented system was developed based on a dynamic object system with message passing and dynamic dispatch. The developed object oriented system includes functions for creating and deleting classes, creating and deleting object instances of a class, functions for working with object properties, functions for adding methods to a class and passing messages. Inheritance is also possible, when a child class will have the properties of the parent class and its own properties. Results. The object oriented system was implemented using a set of macros and helper functions. The basic data structure was a hash table with key access capabilities, which was also implemented using macros. When a class is created, an instance constructor is automatically generated, setting the initial values of the object’s fields. Inheritance is implemented as the inclusion of fields of a parent class in a child object. Polymorphism is implemented as dynamic dispatch: when processing a message, a handler method is searched. The resulting object oriented system, together with the hash table module, occupies 97 lines. Conclusion. As a result of the work, a conceptual model of an object oriented system was created and implemented, which includes the main functions of the object oriented programming paradigm. It has been shown that macros can be used to create a compact object oriented system without the need to add any new functions or capabilities to the functional language interpreter.

Deep learning generative model for crystal structure prediction

Recent advances in deep learning generative models (GMs) have created high capabilities in accessing and assessing complex high-dimensional data, allowing superior efficiency in navigating vast material configuration space in search of viable structures. Coupling such capabilities with physically significant data to construct trained models for materials discovery is crucial to moving this emerging field forward. Here, we present a universal GM for crystal structure prediction (CSP) via a conditional crystal diffusion variational autoencoder (Cond-CDVAE) approach, which is tailored to allow user-defined material and physical parameters such as composition and pressure. This model is trained on an expansive dataset containing over 670,000 local minimum structures, including a rich spectrum of high-pressure structures, along with ambient-pressure structures in Materials Project database. We demonstrate that the Cond-CDVAE model can generate physically plausible structures with high fidelity under diverse pressure conditions without necessitating local optimization, accurately predicting 59.3% of the 3547 unseen ambient-pressure experimental structures within 800 structure samplings, with the accuracy rate climbing to 83.2% for structures comprising fewer than 20 atoms per unit cell. These results meet or exceed those achieved via conventional CSP methods based on global optimization. The present findings showcase substantial potential of GMs in the realm of CSP.

SV4GD: a comprehensive structural variation database specially for genetic diseases.

Structural variations (SVs) contribute to a large extent to genomic diversity and are highly relevant for various human genetic diseases. The sensitivity and specificity of SV identification have significantly improved with the development and widespread application of high-throughput sequencing, making clinical diagnosis and treatment more accurate. Therefore, the SV4GD (Structural Variation for Genetic Diseases, https://bio-computing.hrbmu.edu.cn/SV4GD/), a manually curated database, was constructed to provide a comprehensive, standardized and user-friendly data resource for selective batch browsing, searching, downloading and comparing those genetic disease-relevant SVs. This database compiles 10 305 records of germline structural variants from 58 human neoplastic diseases and 232 non-neoplastic genetic diseases, including 2695 disease-related SVs, and other 7610 pending research SVs detected from patients. SV4GD provides a browser and search engine to query for the detailed information of SVs, human genetic diseases and the clinical information of patients, providing an easy-to-use online tool for clinical and molecular genetics research.

Revisiting huntingtin activity and localization signals in the context of protein structure

Protein localization signals and activity motifs have been defined within huntingtin since 2003. Advances in technology in protein structure determination by cryo-electron microscopy (EM) have led to 2.6 Å resolution structures of huntingtin and HAP40 for the majority of the protein, although structure of the amino terminus with the polyglutamine expansion remains elusive in the context of full-length huntingtin. Recent advances in protein modeling using neural network algorithms trained on a database of known protein structures has resulted in structure predictions that are useful for researchers but need experimental validation. Here, we use both structures solved by cryo-EM as well as modeling centered around experimental structural data to retrospectively revisit huntingtin protein localization signals identified prior to the cryo-EM and AI-enabled structural revolutions. We interrogate these models as well as put forward testable hypotheses of allosteric changes in huntingtin and how they could be affected by polyglutamine expansion. We also extended this methodology to another polyglutamine disease protein, ataxin-1, expanded in Spinocerebellar Ataxia Type 1 (SCA1).

Deep Fuzzy Credibility Surfaces for Integrating External Databases in the Estimation of Operational Value at Risk

Operational risk (OR) is usually caused by losses due to human errors, inadequate or defective internal processes, system failures, or external events that affect an organization. According to the Basel II agreement, OR is defined by seven risk events: internal fraud, external fraud, labor relations, clients, damage to fixed assets, technical failures and failures in the execution and administration of processes. However, the low frequency with which a loss event occurs creates a technological challenge for insurers in estimating the operational value at risk (OpVar) for the protection derived from an organization’s business activities. Following the above, this paper develops and analyzes a Deep Fuzzy Credibility Surface model (DFCS), which allows the integration in a single structure of different loss event databases for the estimation of an operational value at risk (OpVar), overcoming the limitations imposed by the low frequency with which a risk event occurs within an organization (sparse data). For the estimation of OpVar, the DFCS model incorporates a novel activation function based on the generalized log-logistic function to model random variables of frequency and severity that define a loss event (linguistic random variables), as well as a credibility surface to integrate the magnitude and heterogeneity of losses in a single structure as a result of the integration of databases. The stability provided by the DFCS model could be evidenced through the structure exhibited by the aggregate loss distributions (ALDs), which are obtained as a result of the convolution process between frequency and severity random variables for each database and which are expected to achieve similar structures to the probability distributions suggested by Basel II agreements (lean, long tail, positive skewness) against the OR modeling. These features make the DFCS model a reference for estimating the OpVar to protect the risk arising from an organization’s business operations by integrating internal and external loss event databases.

The microbiome diversifies N -acyl lipid pools - including short-chain fatty acid-derived compounds.

N -acyl lipids are important mediators of several biological processes including immune function and stress response. To enhance the detection of N -acyl lipids with untargeted mass spectrometry-based metabolomics, we created a reference spectral library retrieving N -acyl lipid patterns from 2,700 public datasets, identifying 851 N -acyl lipids that were detected 356,542 times. 777 are not documented in lipid structural databases, with 18% of these derived from short-chain fatty acids and found in the digestive tract and other organs. Their levels varied with diet, microbial colonization, and in people living with diabetes. We used the library to link microbial N -acyl lipids, including histamine and polyamine conjugates, to HIV status and cognitive impairment. This resource will enhance the annotation of these compounds in future studies to further the understanding of their roles in health and disease and highlight the value of large-scale untargeted metabolomics data for metabolite discovery.

Structure-guided discovery and rational design of a new poly(ethylene terephthalate) hydrolase from AlphaFold protein structure database

Enzymatic degradation offers a promising eco-friendly solution to plastic pollution, especially for polyethylene terephthalate (PET). Current efforts have focused on screening PET-degrading enzymes from microbial and metagenomic sources and obtaining superior candidates with a limited set of templates. More efficient PET hydrolases are required for PET-waste biorefinery. Here, using a structure-guided bioinformatic workflow, we identified a novel PET hydrolase, LSPET4, from Micromonospora sp. HM5–17, by screening the AlphaFold protein structure database. LSPET4 features a unique carbohydrate-binding module (CBM) and a distinctive linear substrate binding conformation. The intrinsic CBM in LSPET4 exhibited superior binding ability on PET surfaces and enhanced PET hydrolysis performance compared to the previously reported most effective CBM3. Through rational protein engineering focused on stabilizing and modifying the linear substrate binding conformation, we developed LSPET4M6 (D130P, N127F, Y96F, Q209E, A238K, D241S), a variant that achieved a 38.79-fold improvement in activity compared to the wild type, and was comparable to the reported most effective PET hydrolase derived from IsPETase, FAST-PETase at 45 ℃. This variant also demonstrated effectiveness in degrading various commercial PET materials, including PET food sealing films, PET strawberry boxes, and PET tomato boxes used in the food industry. This study not only provides a new template for protein engineering endeavors to create efficient biocatalysts for PET recycling but also offers an effective enzyme discovery approach to uncover enzymes of interest from the AlphaFold protein structure database.

Exploring structural diversity across the protein universe with The Encyclopedia of Domains.

The AlphaFold Protein Structure Database (AFDB) contains more than 214 million predicted protein structures composed of domains, which are independently folding units found in multiple structural and functional contexts. Identifying domains can enable many functional and evolutionary analyses but has remained challenging because of the sheer scale of the data. Using deep learning methods, we have detected and classified every domain in the AFDB, producing The Encyclopedia of Domains. We detected nearly 365 million domains, over 100 million more than can be found by sequence methods, covering more than 1 million taxa. Reassuringly, 77% of the nonredundant domains are similar to known superfamilies, greatly expanding representation of their domain space. We uncovered more than 10,000 new structural interactions between superfamilies and thousands of new folds across the fold space continuum.

Traits.build: A data model, workflow and R package for building harmonised ecological trait databases

Trait databases have proliferated over the past decades, facilitating research on the ecology, evolution, and conservation of taxa across the Tree of Life. Typically, teams of independent researchers build these databases, and each must develop their own workflow and output structure. This divests research hours from downstream tasks such as trait-based analysis and interpretation and the resultant datasets are often difficult to integrate due to disparate database structures. Here we introduce the {traits.build} R-package, which offers a generalised workflow for building trait databases. {traits.build} contains bespoke functions for propagating metadata files, extensive tutorials, and sample configuration files, allowing researchers to efficiently build a new trait database using open source tools. In addition, the {traits.build} output structure is fully documented by a data model, ensuring the meaning of each variable and semantic relationship between variables is transparent and consistent. The data standard links to terms in previously published data standards, drawing strongly on DarwinCore and the Ecological Trait-data Standard, but also includes the ability to fully map location and context properties absent from these vocabularies. It is the first published database-building workflow that adheres to the Extensible Observation Ontology. Simultaneously developing a generalised workflow and publishing a data standard for the workflow provides {traits.build} users a straightforward pathway to build a new trait database that achieves the FAIR principles. The meaning of all variables in a {traits.build} database are already documented, allowing further integration with either other {traits.build} databases or indeed any other database with a documented data model. This follows the vision of the Open Traits Network to build trait databases whose data can be easily integrated for further analysis.