Structure Of Dataset Research Articles

Conf-GEM: A geometric information-assisted direct conformation generation model

Molecular conformations generation (MCG) aims to efficiently obtain reasonable and stable three-dimensional (3D) atomic coordinates of the atoms in the molecule from scratch, providing a structural foundation for molecular representation learning models and advanced downstream molecular design tasks such as molecular property prediction, molecular generation, and molecular docking. Existing MCG methods mostly rely on indirect distance-based strategies, which which can result in geometrically unrealistic conformations, or direct coordinate-based methods, which have larger search spaces and are prone to overfitting. Therefore, this study introduces Conf-GEM, a novel geometric information-assisted direct conformation generation model based on E-GeoGNN, a geometrically augmented 3D graph neural network with multiple scales. Pre-training and divide-and-conquer strategies, are integrated into the proposed model. Conf-GEM outperforms RDKit and nine deep-learning-based MCG models on the GEOM-QM9 and GEOM-Drugs datasets, achieving conformational coverage of 96.69% and 96.07%, respectively, without force field optimization. It also excels on the X-ray diffraction crystal structure dataset with up to 97.04% conformational coverage. In conclusion, Conf-GEM provides a novel solution for stabilizing 3D conformations generation. We provide an online prediction service (https://confgem.cmdrg.com) with a user-friendly interface for researchers.

Steering responsible innovation: the impact of EU REACH legislation on the novelty and toxicity of chemical inventions

ABSTRACT This study investigates the influence of EU Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) legislation on the technological trajectory of the chemical industry. Focusing on the Substances of Very High Concern (SVHC) list, this study used a novel dataset of chemical patents and structures filed at the European Patent Office (EPO) over the period from 1990 to 2016. A staggered difference-in-differences approach with inverse probability weights was used to measure the impact of the SVHC list on the novelty and toxicity of chemical patents. The analysis revealed a substantial increase in the chemical novelty of patents after the introduction of the SVHC list. Simultaneously, a significant decrease in the toxicity of these patents was observed, underpinning the legislative intent of REACH. These findings highlight the ability of regulatory frameworks to strategically steer innovation, reinforcing the alignment between technological advancement and the overarching goals of responsible and sustainable development.

Mapping hydrodynamic structure with sparse or no well data

Hydrodynamic traps are usually mapped using well pressure data to transform structural depth maps, but if well data are sparse then hydrodynamic maps produced this way may have large uncertainties. An alternative approach that does not rely on well data is described here, utilizing simplified, locally planar representation of the potentiometric surface. Ranges of hydraulic gradient magnitudes and azimuths representing different potentiometric surface orientations, together with a range of possible density contrasts between the flowing and trapped fluids, define a three-dimensional array, termed here ‘hydrodynamic space’. This array can be constrained and simplified by reasonable assumptions and the introduction of an additional new concept that combines the hydraulic gradient magnitude and fluid density contrast into a single parameter termed ‘potentiometric transform’. Ranges of these parameters yield a set of hydrodynamic structural maps. The fineness of sampling the hydrodynamic space parameters is limited only by the resources available to support the workflow. The array can be automatically assessed in terms of hydrodynamic trap volumes by applying a structural closure algorithm that isolates and characterizes dip-closed structure. Closure volumes across the map set are ranked by spatial distribution analysis that informs exploration programs relevant to any subsurface fluid management application. The method is described for the first time here and illustrated by application to a real structural dataset.

Computational insights into intrinsically disordered regions in protein-nucleic acid complexes

We study transitions in intrinsically disordered regions (IDRs) upon complex formation, utilizing X-ray-solved structural dataset of protein-DNA and protein-RNA complexes, along with their available unbound protein forms. The identified IDRs are categorized into three classes: Disordered-to-Ordered (D-O), Disordered-to-Partial Ordered (D-PO) and Disordered-to-Disordered (D-D) after comparing them in unbound and complex forms. In the D-O class, IDRs form secondary structures like coils, helices, and strands upon binding to nucleic acids. Though a majority of these IDRs are present at the surface of the complexes, a significant number of IDRs are also observed at the interfaces and are involved in polar interactions. The hydrogen bonds made by the interface IDRs (B_IDRs) with phosphates and bases of nucleic acids are comparatively more than those formed with sugars. B_IDRs form more H-bonds with the ribose in protein-RNA than with the deoxyribose in protein-DNA. Among the B_IDRs, Arg and Lys prefer to interact with the major and minor grooves of DNA and RNA, respectively. Ser, however, prefers the minor groove in both the nucleic acids. Interestingly, we report 61 and 48 IDRs in 31 protein-DNA and 22 protein-RNA complexes, respectively, suggesting nucleic acid binding to proteins may also result in ordered-to-disordered transitions.

They all rock: A systematic comparison of conformational movements in LeuT-fold transporters

Many membrane transporters share the LeuT fold-two five-helix repeats inverted across the membrane plane. Despite hundreds of structures, whether distinct conformational mechanisms are supported by the LeuT fold has not been systematically determined. After annotating published LeuT-fold structures, we analyzed distance difference matrices (DDMs) for nine proteins with multiple available conformations. We identified rigid bodies and relative movements of transmembrane helices (TMs) during distinct steps of the transport cycle. In all transporters, the bundle (first two TMs of each repeat) rotates relative to the hash (third and fourth TMs). Motions of the arms (fifth TM) to close or open the intracellular and outer vestibules are common, as is a TM1a swing, with notable variations in the opening-closing motions of the outer vestibule. Our analyses suggest that LeuT-fold transporters layer distinct motions on a common bundle-hash rock and demonstrate that systematic analyses can provide new insights into large structural datasets.

Perbandingan Dataset Labelled Faces in the Wild (LFW) dan faces94 Menggunakan Algoritma Convolutional Neural Networks (CNN) untuk Pengenalan Wajah

This research compares the performance of two popular datasets, Labelled Faces in the Wild (LFW) and faces94, in the task of face recognition using Convolutional Neural Networks (CNN) algorithms. The LFW dataset is known for its high variation in pose, lighting, and expression, while faces94 is more structured with more uniform lighting and pose conditions. CNNs were chosen for their ability to extract important features from face images for classification. In this study, a CNN model was trained on both datasets and its performance was evaluated using accuracy, precision, and recall metrics. The experimental results showed that the model trained on the faces94 dataset achieved higher accuracy compared to the model trained on the LFW dataset. However, the model on the LFW dataset demonstrated better resilience to variations in lighting and pose conditions. These findings indicate that while a more structured dataset like faces94 can produce a model with high accuracy under testing conditions similar to the training data, a dataset with greater variation like LFW is more suitable for real-world applications involving diverse conditions. This study provides important insights into the selection of datasets for developing robust and accurate face recognition systems.

A cost modelling methodology based on machine learning for engineered-to-order products

Recent scientific studies are targeted at applying and assessing the effectiveness of Machine Learning (ML) approaches for cost estimation during the preliminary design phases. To train ML prediction models, comprehensive and structured datasets of historical data are required. This solution is inapplicable when such information is unavailable or sparse due to the lack of structured datasets. For engineered-to-order products, the number of historical records is often limited and strongly influenced by different purchasing or manufacturing strategies, thus requiring complex normalisation of such data.This method overcomes the above limitations by presenting an ML-based cost modelling methodology for the conceptual design that is applicable even when historical data are insufficient to train the prediction algorithms. The training dataset is generated through an analytical and automatic software tool for manufacturing cost estimation. Such a tool, starting from a 3D model of a product, can quickly and autonomously assess the related cost in different scenarios. An extensive and structured training dataset can be easily generated. The proposed methodology was based on CRISP-DM (Cross Industry Standard Process for Data Mining).Cost engineers of an Oil & Gas company used the method to develop parametric cost models for discs and spacers of an axial compressor. The solution guarantees lower error (7% vs 9%) and significant time-saving (minutes instead of hours) than estimations based on other approaches. Cost models are more comprehensive (capable of analysing different scenarios), explainable (not conceived as a black box), and self-learning (can be updated by extending the training dataset).

A generalized deep neural network approach for improving resolution of fluorescence microscopy images

Deep learning is capable of greatly promoting the progress of super-resolution imaging technology in terms of imaging and reconstruction speed, imaging resolution, and imaging flux. This paper proposes a deep neural network based on a generative adversarial network (GAN). The generator employs a U-Net-based network, which integrates DenseNet for the downsampling component. The proposed method has excellent properties, for example, the network model is trained with several different datasets of biological structures; the trained model can improve the imaging resolution of different microscopy imaging modalities such as confocal imaging and wide-field imaging; and the model demonstrates a generalized ability to improve the resolution of different biological structures even out of the datasets. In addition, experimental results showed that the method improved the resolution of caveolin-coated pits (CCPs) structures from 264[Formula: see text]nm to 138[Formula: see text]nm, a 1.91-fold increase, and nearly doubled the resolution of DNA molecules imaged while being transported through microfluidic channels.

Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture

Accurate recognition of hand-drawn chemical structures is crucial for digitising hand-written chemical information in traditional laboratory notebooks or facilitating stylus-based structure entry on tablets or smartphones. However, the inherent variability in hand-drawn structures poses challenges for existing Optical Chemical Structure Recognition (OCSR) software. To address this, we present an enhanced Deep lEarning for Chemical ImagE Recognition (DECIMER) architecture that leverages a combination of Convolutional Neural Networks (CNNs) and Transformers to improve the recognition of hand-drawn chemical structures. The model incorporates an EfficientNetV2 CNN encoder that extracts features from hand-drawn images, followed by a Transformer decoder that converts the extracted features into Simplified Molecular Input Line Entry System (SMILES) strings. Our models were trained using synthetic hand-drawn images generated by RanDepict, a tool for depicting chemical structures with different style elements. A benchmark was performed using a real-world dataset of hand-drawn chemical structures to evaluate the model's performance. The results indicate that our improved DECIMER architecture exhibits a significantly enhanced recognition accuracy compared to other approaches.Scientific contributionThe new DECIMER model presented here refines our previous research efforts and is currently the only open-source model tailored specifically for the recognition of hand-drawn chemical structures. The enhanced model performs better in handling variations in handwriting styles, line thicknesses, and background noise, making it suitable for real-world applications. The DECIMER hand-drawn structure recognition model and its source code have been made available as an open-source package under a permissive license.Graphical

Testing rational expectations in a cointegrated VAR with structural change

The rational expectation hypothesis is widely used in finance and macroeconomics. A research question investigates whether models using this hypothesis can fit the data well. Researchers have been developing econometric procedures to test rational expectation models. Sargent and Hansen (1991) proposed how to test exact rational expectation restrictions. This study aims to achieve three objectives. The first objective is to extend Johansen and Swensen’s framework that test exact rational expectation restrictions to the case where the data-generating process is a cointegrated vector autoregressive model with abrupt structural change (CVAR-SC). The second goal is to highlight that the type of rational expectation restrictions analysed in this paper imply co-breaking, as defined by Hendry and Mizon (1998). Finally, the restrictions on the CVAR-SC parameters implied by the present value model and expectation hypothesis of the interest rate term structure are analysed, derived and tested. Three empirical exercises are reported. The first is Engsted (2002)’s dataset on share prices and dividends, the second uses a Brazilian term structure data set and the third uses a North American term structure dataset.

Towards a dependable data set of structures for L-asparaginase research.

The Protein Data Bank (PDB) includes a carefully curated treasury of experimentally derived structural data on biological macromolecules and their various complexes. Such information is fundamental for a multitude of projects that involve large-scale data mining and/or detailed evaluation of individual structures of importance to chemistry, biology and, most of all, to medicine, where it provides the foundation for structure-based drug discovery. However, despite extensive validation mechanisms, it is almost inevitable that among the ∼215 000 entries there will occasionally be suboptimal or incorrect structure models. It is thus vital to apply careful verification procedures to those segments of the PDB that are of direct medicinal interest. Here, such an analysis was carried out for crystallographic models of L-asparaginases, enzymes that include approved drugs for the treatment of certain types of leukemia. The focus was on the adherence of the atomic coordinates to the rules of stereochemistry and their agreement with the experimental electron-density maps. Whereas the current clinical application of L-asparaginases is limited to two bacterial proteins and their chemical modifications, the field of investigations of such enzymes has expanded tremendously in recent years with the discovery of three entirely different structural classes and with numerous reports, not always quite reliable, of the anticancer properties of L-asparaginases of different origins.

Machine learning enhanced analysis of EBSD data for texture representation

Generating reduced-order, synthetic grain structure datasets that accurately represent the measured grain structure of a material is important for reducing the cost and increasing the accuracy of computational crystal plasticity efforts. This study introduces a machine-learning-based approach, termed texture adaptive clustering and sampling (TACS), for generating representative Euler angle datasets that accurately mimic the crystallographic texture. The TACS approach employs K-means clustering and density-based sampling in a closed-loop iteration to create representative Euler angle datasets. Proof-of-principle experiments were performed on rolled and recrystallized low-carbon steel. Validation of the TACS approach was extended to twenty-two datasets, varying lattice structures, and complex crystallographic textures, thereby encompassing a broad range of materials and crystal structures. Kolmogorov-Smirnov (K-S) test comparisons underscore the performance of the TACS approach over traditional electron backscatter diffraction EBSD dataset reduction techniques, with average K-S test scores nearing 0.9, indicating a high-fidelity representation of the original datasets. In contrast, conventional methods display scores below 0.3, indicating less reliability of the structure representation. The independence of the TACS approach from material texture and its capability to autonomously generate datasets with predetermined data points demonstrates its unbiased potential in streamlining dataset preparation for crystallographic analysis.

PRFold-TNN: Protein Fold Recognition With an Ensemble Feature Selection Method Using PageRank Algorithm Based on Transformer.

Understanding the tertiary structures of proteins is of great benefit to function in many aspects of human life. Protein fold recognition is a vital and salient means to know protein structure. Until now, researchers have successively proposed a variety of methods to realize protein fold recognition, but the novel and effective computational method is still needed to handle this problem with the continuous updating of protein structure databases. In this study, we develop a new protein structure dataset named AT and propose the PRFold-TNN model for protein fold recognition. Firstly, different types of feature extraction methods including AAC, HMM, HMM-Bigram and ACC are selected to extract corresponding features for protein sequences. Then an ensemble feature selection method based on PageRank algorithm integrating various tree-based algorithms is used to screen the fusion features. Ultimately, the classifier based on the Transformer model achieves the final prediction. Experiments show that the prediction accuracy is 86.27% on the AT dataset and 88.91% on the independent test set, indicating that the model can demonstrate superior performance and generalization ability in the problem of protein fold recognition. Furthermore, we also carry out research on the DD, EDD and TG benchmark datasets, and make them achieve prediction accuracy of 88.41%, 97.91% and 95.16%, which are at least 3.0%, 0.8% and 2.5% higher than those of the state-of-the-art methods. It can be concluded that the PRFold-TNN model is more prominent.

Multi-scale image-based damage recognition and assessment for reinforced concrete structures in post-earthquake emergency response

As cities continue to develop, the significance of resilience and intelligence is increasing. In post-earthquake emergency response, there is a continuous demand for efficient and accurate methods of structural state assessment. This study employs deep learning (DL) techniques to propose a multi-scale image-based damage recognition and assessment method for reinforced concrete (RC) structures. First, an RC structural damage recognition task framework and a structural mechanical damage image dataset are established. Second, a DL model is selected to conduct the experiments and enhance its performance through transfer learning. Then, a multi-scale correlated structural state assessment procedure is introduced where local, component, structural, and regional scales are linked. Finally, an engineering case is presented to describe the application steps of the method in real-world scenarios, demonstrating its feasibility. Nevertheless, the proposed method lacks comprehensive validation across various geotechnical conditions and detailed structural configurations, which may limit its generalizability. This study has the potential to enhance the efficiency and scope of post-disaster emergency response and contribute to the development of sustainable cities.

Folding the human proteome using BioNeMo: A fused dataset of structural models for machine learning purposes

Human proteins are crucial players in both health and disease. Understanding their molecular landscape is a central topic in biological research. Here, we present an extensive dataset of predicted protein structures for 42,042 distinct human proteins, including splicing variants, derived from the UniProt reference proteome UP000005640. To ensure high quality and comparability, the dataset was generated by combining state-of-the-art modeling-tools AlphaFold 2, OpenFold, and ESMFold, provided within NVIDIA’s BioNeMo platform, as well as homology modeling using Innophore’s CavitomiX platform. Our dataset is offered in both unedited and edited formats for diverse research requirements. The unedited version contains structures as generated by the different prediction methods, whereas the edited version contains refinements, including a dataset of structures without low prediction-confidence regions and structures in complex with predicted ligands based on homologs in the PDB. We are confident that this dataset represents the most comprehensive collection of human protein structures available today, facilitating diverse applications such as structure-based drug design and the prediction of protein function and interactions.

Accurate Prediction of NMR Chemical Shifts: Integrating DFT Calculations with Three-Dimensional Graph Neural Networks.

Computer prediction of NMR chemical shifts plays an increasingly important role in molecular structure assignment and elucidation for organic molecule studies. Density functional theory (DFT) and gauge-including atomic orbital (GIAO) have established a framework to predict NMR chemical shifts but often at a significant computational expense with a limited prediction accuracy. Recent advancements in deep learning methods, especially graph neural networks (GNNs), have shown promise in improving the accuracy of predicting experimental chemical shifts, either by using 2D molecular topological features or 3D conformational representation. This study presents a new 3D GNN model to predict 1H and 13C chemical shifts, CSTShift, that combines atomic features with DFT-calculated shielding tensor descriptors, capturing both isotropic and anisotropic shielding effects. Utilizing the NMRShiftDB2 data set and conducting DFT optimization and GIAO calculations at the B3LYP/6-31G(d) level, we prepared the NMRShiftDB2-DFT data set of high-quality 3D structures and shielding tensors with corresponding experimentally measured 1H and 13C chemical shifts. The developed CSTShift models achieve the state-of-the-art prediction performance on both the NMRShiftDB2-DFT test data set and external CHESHIRE data set. Further case studies on identifying correct structures from two groups of constitutional isomers show its capability for structure assignment and elucidation. The source code and data are accessible at https://yzhang.hpc.nyu.edu/IMA.

Recommendations of the SUFU/AUGS/ICS Female Stress Urinary Incontinence Surgical Publication Working Group: A Common Standard Minimum Data Set for the Literature.

Relevant, meaningful, and achievable data points are critical in objectively assessing quality, utility, and outcomes in female stress urinary incontinence (SUI) surgery. A minimum data set female SUI surgery studies was proposed by the first American Urological Association guidelines on the surgical management of female SUI in 1997, but recommendation adherence has been suboptimal. The Female Stress Urinary Incontinence Surgical Publication Working Group (WG) was created from members of several prominent organizations to formulate a recommended standard of study structure, description, and minimum outcome data set to be utilized in designing and publishing future SUI studies. The goal of this WG was to create a body of evidence better able to assess the outcomes of female SUI surgery. The WG reviewed the minimum data set proposed in the 1997 AUA SUI Guideline document, and other relevant literature. The body of literature was examined in the context of the profound changes in the field over the past 25 years. Through a DELPHI process, a standard study structure and minimum data set were generated. Care was taken to balance the value of several meaningful and relevant data points against the burden of creating an excessively difficult or restrictive standard that would disincentivize widespread adoption and negatively impact manuscript production and acceptance. The WG outlined standardization in four major areas: 1) study design, 2) pretreatment demographics and characterization of the study population, 3) intraoperative events, and 4) post-treatment evaluation, and complications. Forty-two items were evaluated and graded as: STANDARD - must be included; ADDITIONAL - may be included for a specific study and is inclusive of the Standard items; OPTIMAL - may be included for a comprehensive study and is inclusive of the Standard and Additional items; UNNECESSARY/LEGACY - not relevant. A reasonable, achievable, and clinically meaningful minimum data set has been constructed. A structured framework will allow future surgical interventions for female SUI to be objectively scrutinized and compared in a clinically significant manner. Ultimately, such a data set, if adopted by the academic community, will enhance the quality of the scientific literature, and ultimately improve short and long-term outcomes for female patients undergoing surgery to correct SUI.

Recommendations of the SUFU/AUGS/ICS Female Stress Urinary Incontinence Surgical Publication Working Group: A common standard minimum data set for the literature.

Relevant, meaningful, and achievable data points are critical in objectively assessing quality, utility, and outcomes in female stress urinary incontinence (SUI) surgery. A minimum data set female SUI surgery studies was proposed by the first American Urological Association guidelines on the surgical management of female SUI in 1997, but recommendation adherence has been suboptimal. The Female Stress Urinary Incontinence Surgical Publication Working Group (WG) was created from members of several prominent organizations to formulate a recommended standard of study structure, description, and minimum outcome data set to be utilized in designing and publishing future SUI studies. The goal of this WG was to create a body of evidence better able to assess the outcomes of female SUI surgery. The WG reviewed the minimum data set proposed in the 1997 AUA SUI Guideline document, and other relevant literature. The body of literature was examined in the context of the profound changes in the field over the past 25 years. Through a DELPHI process, a standard study structure and minimum data set were generated. Care was taken to balance the value of several meaningful and relevant data points against the burden of creating an excessively difficult or restrictive standard that would disincentivize widespread adoption and negatively impact manuscript production and acceptance. The WG outlined standardization in four major areas: (1) study design, (2) pretreatment demographics and characterization of the study population, (3) intraoperative events, and (4) posttreatment evaluation, and complications. Forty-two items were evaluated and graded as: STANDARD-must be included; ADDITIONAL-may be included for a specific study and is inclusive of the Standard items; OPTIMAL-may be included for a comprehensive study and is inclusive of the Standard and Additional items; UNNECESSARY/LEGACY-not relevant. A reasonable, achievable, and clinically meaningful minimum data set has been constructed. A structured framework will allow future surgical interventions for female SUI to be objectively scrutinized and compared in a clinically significant manner. Ultimately, such a data set, if adopted by the academic community, will enhance the quality of the scientific literature, and ultimately improve short and long-term outcomes for female patients undergoing surgery to correct SUI.

Universal Machine Learning Kohn–Sham Hamiltonian for Materials

Abstract While density functional theory (DFT) serves as a prevalent computational approach in electronic structure calculations, its computational demands and scalability limitations persist. Recently, leveraging neural networks to parameterize the Kohn–Sham DFT Hamiltonian has emerged as a promising avenue for accelerating electronic structure computations. Despite advancements, challenges such as the necessity for computing extensive DFT training data to explore each new system and the complexity of establishing accurate machine learning models for multi-elemental materials still exist. Addressing these hurdles, this study introduces a universal electronic Hamiltonian model trained on Hamiltonian matrices obtained from first-principles DFT calculations of nearly all crystal structures on the Materials Project. We demonstrate its generality in predicting electronic structures across the whole periodic table, including complex multi-elemental systems, solid-state electrolytes, Moiré twisted bilayer heterostructure, and metal-organic frameworks. Moreover, we utilize the universal model to conduct high-throughput calculations of electronic structures for crystals in GNoME datasets, identifying 3940 crystals with direct band gaps and 5109 crystals with flat bands. By offering a reliable efficient framework for computing electronic properties, this universal Hamiltonian model lays the groundwork for advancements in diverse fields, such as easily providing a huge data set of electronic structures and also making the materials design across the whole periodic table possible.

Recommendations of the SUFU/AUGS/ICS female stress urinary incontinence surgical publication working group: A common standard minimum data set for the literature

Introduction and ObjectivesRelevant, meaningful, and achievable data points are critical in objectively assessing quality, utility, and outcomes in female stress urinary incontinence (SUI) surgery. A minimum data set female SUI surgery studies was proposed by the first American Urological Association guidelines on the surgical management of female SUI in 1997, but recommendation adherence has been suboptimal The Female Stress Urinary Incontinence Surgical Publication Working Group (WG) was created from members of several prominent organizations to formulate a recommended standard of study structure, description, and minimum outcome data set to be utilized in designing and publishing future SUI studies. The goal of this WG was to create a body of evidence better able to assess the outcomes of female SUI surgery. MethodsThe WG reviewed the minimum data set proposed in the 1997 AUA SUI Guideline document, and other relevant literature. The body of literature was examined in the context of the profound changes in the field over the past 25 years. Through a DELPHI process, a standard study structure and minimum data set were generated. Care was taken to balance the value of several meaningful and relevant data points against the burden of creating an excessively difficult or restrictive standard that would disincentivize widespread adoption and negatively impact manuscript production and acceptance. Results:The WG outlined standardization in four major areas: (1) study design, (2) pretreatment demographics and characterization of the study population, (3) intraoperative events, and (4) post-treatment evaluation, and complications. Forty-two items were evaluated and graded as: STANDARD — must be included; ADDITIONAL — may be included for a specific study and is inclusive of the Standard items; OPTIMAL — may be included for a comprehensive study and is inclusive of the Standard and Additional items; UNNECESSARY/LEGACY — not relevant. Conclusion:A reasonable, achievable, and clinically meaningful minimum data set has been constructed. A structured framework will allow future surgical interventions for female SUI to be objectively scrutinized and compared in a clinically significant manner. Ultimately, such a data set, if adopted by the academic community, will enhance the quality of the scientific literature, and ultimately improve short and long-term outcomes for female patients undergoing surgery to correct SUI.